Merge pull request 'pv_sizing_modelling' (#21) from pv_sizing_modelling into main

Reviewed-on: #21

.gitignore

@@ -12,3 +12,5 @@
 cerc_hub.egg-info
 /out_files
 /input_files/output_buildings.geojson
+*/.pyc
+*.pyc

.idea/energy_system_modelling_workflow.iml

@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="hub" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.9 (hub)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

.idea/misc.xml

@@ -1,4 +1,7 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="hub" project-jdk-type="Python SDK" />
+  <component name="Black">
+    <option name="sdkUUID" value="97386509-ec9b-4dd7-929b-7585219c0447" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (hub)" project-jdk-type="Python SDK" />
 </project>

building_modelling/ep_run_enrich.py

@@ -6,13 +6,13 @@ import csv
 from hub.exports.energy_building_exports_factory import EnergyBuildingsExportsFactory
 from hub.imports.results_factory import ResultFactory
 
-sys.path.append('./')
+sys.path.append('../energy_system_modelling_package/')
 
 
-def energy_plus_workflow(city):
+def energy_plus_workflow(city, output_path):
   try:
     # city = city
-    out_path = (Path(__file__).parent.parent / 'out_files')
+    out_path = output_path
     files = glob.glob(f'{out_path}/*')
 
     # for file in files:

building_modelling/geojson_creator.py

@@ -4,13 +4,16 @@ from shapely import Point
 from pathlib import Path
 
 
-def process_geojson(x, y, diff):
+def process_geojson(x, y, diff, path, expansion=False):
   selection_box = Polygon([[x + diff, y - diff],
                            [x - diff, y - diff],
                            [x - diff, y + diff],
                            [x + diff, y + diff]])
-  geojson_file = Path('./data/collinear_clean 2.geojson').resolve()
-  output_file = Path('./input_files/output_buildings.geojson').resolve()
+  geojson_file = Path(path / 'data/collinear_clean 2.geojson').resolve()
+  if not expansion:
+    output_file = Path(path / 'input_files/output_buildings.geojson').resolve()
+  else:
+    output_file = Path(path / 'input_files/output_buildings_expanded.geojson').resolve()
   buildings_in_region = []
 
   with open(geojson_file, 'r') as file:

central.py

@@ -1,61 +0,0 @@
-import pandas as pd
-from scripts.geojson_creator import process_geojson
-from pathlib import Path
-from hub.imports.geometry_factory import GeometryFactory
-from hub.helpers.dictionaries import Dictionaries
-from hub.imports.construction_factory import ConstructionFactory
-from hub.imports.usage_factory import UsageFactory
-from hub.imports.weather_factory import WeatherFactory
-from hub.imports.results_factory import ResultFactory
-from scripts import random_assignation
-from hub.imports.energy_systems_factory import EnergySystemsFactory
-from scripts.energy_system_sizing_and_simulation_factory import EnergySystemsSimulationFactory
-from scripts.costs.cost import Cost
-from scripts.costs.constants import SYSTEM_RETROFIT_AND_PV
-from hub.exports.exports_factory import ExportsFactory
-
-# Specify the GeoJSON file path
-location = [45.49034212153445, -73.61435648647083]
-geojson_file = process_geojson(x=location[1], y=location[0], diff=0.0001)
-file_path = (Path(__file__).parent / 'input_files' / 'processed_output -single_building.geojson')
-# Specify the output path for the PDF file
-output_path = (Path(__file__).parent / 'out_files').resolve()
-# Create city object from GeoJSON file
-city = GeometryFactory('geojson',
-                       path=file_path,
-                       height_field='height',
-                       year_of_construction_field='year_of_construction',
-                       function_field='function',
-                       function_to_hub=Dictionaries().montreal_function_to_hub_function).city
-# Enrich city data
-ConstructionFactory('nrcan', city).enrich()
-
-UsageFactory('nrcan', city).enrich()
-WeatherFactory('epw', city).enrich()
-ResultFactory('energy_plus_multiple_buildings', city, output_path).enrich()
-ExportsFactory('sra', city, output_path).export()
-random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
-EnergySystemsFactory('montreal_future', city).enrich()
-for building in city.buildings:
-  EnergySystemsSimulationFactory('archetype13', building=building, output_path=output_path).enrich()
-sum_floor_area = 0
-buildings_list = []
-for building in city.buildings:
-  buildings_list.append(building.name)
-df = pd.DataFrame(columns=['building_name', 'total_floor_area', 'investment_cost', 'lc CAPEX'])
-df['building_name'] = buildings_list
-for building in city.buildings:
-  for thermal_zone in building.thermal_zones_from_internal_zones:
-    sum_floor_area += thermal_zone.total_floor_area
-  costs = Cost(building=building, retrofit_scenario=SYSTEM_RETROFIT_AND_PV).life_cycle
-  costs.loc['global_capital_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].to_csv(
-    output_path / f'{building.name}_cc.csv')
-  investment_cost = costs.loc['global_capital_costs',
-                              f'Scenario {SYSTEM_RETROFIT_AND_PV}'].loc[0, 'D3020_heat_and_cooling_generating_systems']
-  lcc_capex = costs.loc['total_capital_costs_systems', f'Scenario {SYSTEM_RETROFIT_AND_PV}']
-  df.loc[df['building_name'] == building.name, 'total_floor_area'] = (
-    building.thermal_zones_from_internal_zones[0].total_floor_area)
-  df.loc[df['building_name'] == building.name, 'investment_cost'] = investment_cost
-  df.loc[df['building_name'] == building.name, 'lc CAPEX'] = lcc_capex
-
-df.to_csv(output_path / 'economic analysis.csv')

costing_package/capital_costs.py

@@ -11,10 +11,10 @@ import pandas as pd
 import numpy_financial as npf
 from hub.city_model_structure.building import Building
 import hub.helpers.constants as cte
-from scripts.costs.configuration import Configuration
-from scripts.costs.constants import (SKIN_RETROFIT, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV,
-                                     SYSTEM_RETROFIT_AND_PV, CURRENT_STATUS, PV)
-from scripts.costs.cost_base import CostBase
+from costing_package.configuration import Configuration
+from costing_package.constants import (SKIN_RETROFIT, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV,
+                             SYSTEM_RETROFIT_AND_PV, CURRENT_STATUS, PV, SYSTEM_RETROFIT)
+from costing_package.cost_base import CostBase
 
 
 class CapitalCosts(CostBase):
@@ -32,10 +32,12 @@ class CapitalCosts(CostBase):
         'B3010_opaque_roof',
         'B1010_superstructure',
         'D2010_photovoltaic_system',
-        'D3020_heat_and_cooling_generating_systems',
-        'D3040_distribution_systems',
-        'D3050_other_hvac_ahu',
-        'D3060_storage_systems',
+        'D3020_simultaneous_heat_and_cooling_generating_systems',
+        'D3030_heating_systems',
+        'D3040_cooling_systems',
+        'D3050_distribution_systems',
+        'D3060_other_hvac_ahu',
+        'D3070_storage_systems',
         'D40_dhw',
       ],
       dtype='float'
@@ -45,10 +47,12 @@ class CapitalCosts(CostBase):
     self._yearly_capital_costs.loc[0, 'B3010_opaque_roof'] = 0
     self._yearly_capital_costs.loc[0, 'B1010_superstructure'] = 0
     self._yearly_capital_costs.loc[0, 'D2010_photovoltaic_system'] = 0
-    self._yearly_capital_costs.loc[0, 'D3020_heat_and_cooling_generating_systems'] = 0
-    self._yearly_capital_costs.loc[0, 'D3040_distribution_systems'] = 0
-    self._yearly_capital_costs.loc[0, 'D3080_other_hvac_ahu'] = 0
-    self._yearly_capital_costs.loc[0, 'D3060_storage_systems'] = 0
+    self._yearly_capital_costs.loc[0, 'D3020_simultaneous_heat_and_cooling_generating_systems'] = 0
+    self._yearly_capital_costs.loc[0, 'D3030_heating_systems'] = 0
+    self._yearly_capital_costs.loc[0, 'D3040_cooling_systems'] = 0
+    self._yearly_capital_costs.loc[0, 'D3050_distribution_systems'] = 0
+    self._yearly_capital_costs.loc[0, 'D3060_other_hvac_ahu'] = 0
+    self._yearly_capital_costs.loc[0, 'D3070_storage_systems'] = 0
     self._yearly_capital_costs.loc[0, 'D40_dhw'] = 0
 
     self._yearly_capital_incomes = pd.DataFrame(
@@ -107,7 +111,7 @@ class CapitalCosts(CostBase):
     capital_cost_transparent = surface_transparent * chapter.item('B2020_transparent').refurbishment[0]
     capital_cost_roof = surface_roof * chapter.item('B3010_opaque_roof').refurbishment[0]
     capital_cost_ground = surface_ground * chapter.item('B1010_superstructure').refurbishment[0]
-    if self._configuration.retrofit_scenario not in (SYSTEM_RETROFIT_AND_PV, CURRENT_STATUS, PV):
+    if self._configuration.retrofit_scenario not in (SYSTEM_RETROFIT_AND_PV, CURRENT_STATUS, PV, SYSTEM_RETROFIT):
       self._yearly_capital_costs.loc[0, 'B2010_opaque_walls'] = capital_cost_opaque * self._own_capital
       self._yearly_capital_costs.loc[0, 'B2020_transparent'] = capital_cost_transparent * self._own_capital
       self._yearly_capital_costs.loc[0, 'B3010_opaque_roof'] = capital_cost_roof * self._own_capital
@@ -151,16 +155,23 @@ class CapitalCosts(CostBase):
     chapter = self._capital_costs_chapter.chapter('D_services')
     system_components, component_categories, component_sizes = self.system_components()
     capital_cost_heating_and_cooling_equipment = 0
+    capital_cost_heating_equipment = 0
+    capital_cost_cooling_equipment = 0
     capital_cost_domestic_hot_water_equipment = 0
     capital_cost_energy_storage_equipment = 0
     capital_cost_distribution_equipment = 0
     capital_cost_lighting = 0
-    capital_cost_pv = 0
+    capital_cost_pv = self._surface_pv * chapter.item('D2010_photovoltaic_system').initial_investment[0]
     for (i, component) in enumerate(system_components):
-      if component_categories[i] == 'generation':
+      if component_categories[i] == 'multi_generation':
         capital_cost_heating_and_cooling_equipment += chapter.item(component).initial_investment[0] * component_sizes[i]
+      elif component_categories[i] == 'heating':
+        capital_cost_heating_equipment += chapter.item(component).initial_investment[0] * component_sizes[i]
+      elif component_categories[i] == 'cooling':
+        capital_cost_cooling_equipment += chapter.item(component).initial_investment[0] * component_sizes[i]
       elif component_categories[i] == 'dhw':
-        capital_cost_domestic_hot_water_equipment += 0
+        capital_cost_domestic_hot_water_equipment += chapter.item(component).initial_investment[0] * \
+                                                      component_sizes[i]
       elif component_categories[i] == 'distribution':
         capital_cost_distribution_equipment += chapter.item(component).initial_investment[0] * \
                                                       component_sizes[i]
@@ -169,19 +180,25 @@ class CapitalCosts(CostBase):
 
     if self._configuration.retrofit_scenario in (SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT_AND_PV, PV):
       self._yearly_capital_costs.loc[0, 'D2010_photovoltaic_system'] = capital_cost_pv
-    if self._configuration.retrofit_scenario in (SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV):
-      self._yearly_capital_costs.loc[0, 'D3020_heat_and_cooling_generating_systems'] = (
+    if (self._configuration.retrofit_scenario in
+       (SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT)):
+      self._yearly_capital_costs.loc[0, 'D3020_simultaneous_heat_and_cooling_generating_systems'] = (
           capital_cost_heating_and_cooling_equipment * self._own_capital)
-      self._yearly_capital_costs.loc[0, 'D3040_distribution_systems'] = (
+      self._yearly_capital_costs.loc[0, 'D3030_heating_systems'] = (
+        capital_cost_heating_equipment * self._own_capital)
+      self._yearly_capital_costs.loc[0, 'D3040_cooling_systems'] = (
+        capital_cost_cooling_equipment * self._own_capital)
+      self._yearly_capital_costs.loc[0, 'D3050_distribution_systems'] = (
           capital_cost_distribution_equipment * self._own_capital)
-      self._yearly_capital_costs.loc[0, 'D3060_storage_systems'] = (
+      self._yearly_capital_costs.loc[0, 'D3070_storage_systems'] = (
           capital_cost_energy_storage_equipment * self._own_capital)
       self._yearly_capital_costs.loc[0, 'D40_dhw'] = (
           capital_cost_domestic_hot_water_equipment * self._own_capital)
     capital_cost_hvac = (capital_cost_heating_and_cooling_equipment + capital_cost_distribution_equipment +
                          capital_cost_energy_storage_equipment + capital_cost_domestic_hot_water_equipment)
-    return (capital_cost_pv, capital_cost_heating_and_cooling_equipment, capital_cost_distribution_equipment,
-            capital_cost_energy_storage_equipment, capital_cost_domestic_hot_water_equipment, capital_cost_lighting, capital_cost_hvac)
+    return (capital_cost_pv, capital_cost_heating_and_cooling_equipment, capital_cost_heating_equipment,
+            capital_cost_distribution_equipment, capital_cost_cooling_equipment, capital_cost_energy_storage_equipment,
+            capital_cost_domestic_hot_water_equipment, capital_cost_lighting, capital_cost_hvac)
 
   def yearly_energy_system_costs(self):
     chapter = self._capital_costs_chapter.chapter('D_services')
@@ -201,59 +218,89 @@ class CapitalCosts(CostBase):
           system_investment_costs[0] * self._configuration.percentage_credit
         )
       )
-      self._yearly_capital_costs.loc[year, 'D3020_heat_and_cooling_generating_systems'] = (
+      self._yearly_capital_costs.loc[year, 'D3020_simultaneous_heat_and_cooling_generating_systems'] = (
         -npf.pmt(
           self._configuration.interest_rate,
           self._configuration.credit_years,
           system_investment_costs[1] * self._configuration.percentage_credit
         )
       )
-      self._yearly_capital_costs.loc[year, 'D3040_distribution_systems'] = (
+      self._yearly_capital_costs.loc[year, 'D3030_heating_systems'] = (
         -npf.pmt(
           self._configuration.interest_rate,
           self._configuration.credit_years,
           system_investment_costs[2] * self._configuration.percentage_credit
         )
       )
-      self._yearly_capital_costs.loc[year, 'D3060_storage_systems'] = (
+      self._yearly_capital_costs.loc[year, 'D3040_cooling_systems'] = (
         -npf.pmt(
           self._configuration.interest_rate,
           self._configuration.credit_years,
           system_investment_costs[3] * self._configuration.percentage_credit
         )
       )
-      self._yearly_capital_costs.loc[year, 'D40_dhw'] = (
+      self._yearly_capital_costs.loc[year, 'D3050_distribution_systems'] = (
         -npf.pmt(
           self._configuration.interest_rate,
           self._configuration.credit_years,
           system_investment_costs[4] * self._configuration.percentage_credit
         )
       )
+      self._yearly_capital_costs.loc[year, 'D3070_storage_systems'] = (
+        -npf.pmt(
+          self._configuration.interest_rate,
+          self._configuration.credit_years,
+          system_investment_costs[5] * self._configuration.percentage_credit
+        )
+      )
+      self._yearly_capital_costs.loc[year, 'D40_dhw'] = (
+        -npf.pmt(
+          self._configuration.interest_rate,
+          self._configuration.credit_years,
+          system_investment_costs[6] * self._configuration.percentage_credit
+        )
+      )
       if self._configuration.retrofit_scenario not in (SKIN_RETROFIT, PV):
         for (i, component) in enumerate(system_components):
           if (year % chapter.item(component).lifetime) == 0 and year != (self._configuration.number_of_years - 1):
-            if component_categories[i] == 'generation':
-              reposition_cost_heating_and_cooling_equipment = chapter.item(component).reposition[0] * component_sizes[i] * costs_increase
-              self._yearly_capital_costs.loc[year, 'D3020_heat_and_cooling_generating_systems'] += reposition_cost_heating_and_cooling_equipment
+            if component_categories[i] == 'multi_generation':
+              reposition_cost_heating_and_cooling_equipment = (chapter.item(component).reposition[0] *
+                                                               component_sizes[i] * costs_increase)
+              self._yearly_capital_costs.loc[year, 'D3020_simultaneous_heat_and_cooling_generating_systems'] += (
+                reposition_cost_heating_and_cooling_equipment)
+            elif component_categories[i] == 'heating':
+              reposition_cost_heating_equipment = (chapter.item(component).reposition[0] *
+                                                   component_sizes[i] * costs_increase)
+              self._yearly_capital_costs.loc[year, 'D3030_heating_systems'] += (
+                reposition_cost_heating_equipment)
+            elif component_categories[i] == 'cooling':
+              reposition_cost_cooling_equipment = (chapter.item(component).reposition[0] *
+                                                   component_sizes[i] * costs_increase)
+              self._yearly_capital_costs.loc[year, 'D3040_cooling_systems'] += (
+                reposition_cost_cooling_equipment)
             elif component_categories[i] == 'dhw':
-              reposition_cost_domestic_hot_water_equipment = 0
+              reposition_cost_domestic_hot_water_equipment = (
+                  chapter.item(component).reposition[0] * component_sizes[i] * costs_increase)
               self._yearly_capital_costs.loc[year, 'D40_dhw'] += reposition_cost_domestic_hot_water_equipment
             elif component_categories[i] == 'distribution':
-              reposition_cost_distribution_equipment = chapter.item(component).reposition[0] * component_sizes[i] * costs_increase
-              self._yearly_capital_costs.loc[year, 'D3040_distribution_systems'] += reposition_cost_distribution_equipment
+              reposition_cost_distribution_equipment = (
+                  chapter.item(component).reposition[0] * component_sizes[i] * costs_increase)
+              self._yearly_capital_costs.loc[year, 'D3050_distribution_systems'] += (
+                reposition_cost_distribution_equipment)
             else:
-              reposition_cost_energy_storage_equipment = chapter.item(component).initial_investment[0] * component_sizes[i] * costs_increase
-              self._yearly_capital_costs.loc[year, 'D3060_storage_systems'] += reposition_cost_energy_storage_equipment
+              reposition_cost_energy_storage_equipment = (
+                  chapter.item(component).initial_investment[0] * component_sizes[i] * costs_increase)
+              self._yearly_capital_costs.loc[year, 'D3070_storage_systems'] += reposition_cost_energy_storage_equipment
       if self._configuration.retrofit_scenario == CURRENT_STATUS and pv:
         if (year % chapter.item('D2010_photovoltaic_system').lifetime) == 0:
           self._yearly_capital_costs.loc[year, 'D2010_photovoltaic_system'] += (
-              self._surface_pv * 0 * costs_increase
+              self._surface_pv * chapter.item('D2010_photovoltaic_system').reposition[0] * costs_increase
           )
       elif self._configuration.retrofit_scenario in (PV, SYSTEM_RETROFIT_AND_PV,
                                                      SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV):
         if (year % chapter.item('D2010_photovoltaic_system').lifetime) == 0:
           self._yearly_capital_costs.loc[year, 'D2010_photovoltaic_system'] += (
-              self._surface_pv * 0 * costs_increase
+              self._surface_pv * chapter.item('D2010_photovoltaic_system').reposition[0] * costs_increase
           )
 
   def system_components(self):
@@ -279,8 +326,11 @@ class CapitalCosts(CostBase):
               system_components.append(self.boiler_type(generation_system))
             else:
               system_components.append('D302010_template_heat')
-          elif cte.HEATING or cte.COOLING in demand_types:
-            component_categories.append('generation')
+          elif cte.HEATING in demand_types:
+            if cte.COOLING in demand_types and generation_system.fuel_type == cte.ELECTRICITY:
+              component_categories.append('multi_generation')
+            else:
+              component_categories.append('heating')
             sizes.append(installed_capacity)
             if generation_system.system_type == cte.HEAT_PUMP:
               item_type = self.heat_pump_type(generation_system)
@@ -289,11 +339,18 @@ class CapitalCosts(CostBase):
               item_type = self.boiler_type(generation_system)
               system_components.append(item_type)
             else:
-              if cte.COOLING in demand_types and cte.HEATING not in demand_types:
+              if cooling_capacity > heating_capacity:
                 system_components.append('D302090_template_cooling')
               else:
                 system_components.append('D302010_template_heat')
-
+          elif cte.COOLING in demand_types:
+            component_categories.append('cooling')
+            sizes.append(installed_capacity)
+            if generation_system.system_type == cte.HEAT_PUMP:
+              item_type = self.heat_pump_type(generation_system)
+              system_components.append(item_type)
+            else:
+              system_components.append('D302090_template_cooling')
           if generation_system.energy_storage_systems is not None:
             energy_storage_systems = generation_system.energy_storage_systems
             for storage_system in energy_storage_systems:

costing_package/constants.py

@@ -12,10 +12,12 @@ SKIN_RETROFIT = 1
 SYSTEM_RETROFIT_AND_PV = 2
 SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV = 3
 PV = 4
+SYSTEM_RETROFIT = 5
 RETROFITTING_SCENARIOS = [
   CURRENT_STATUS,
   SKIN_RETROFIT,
   SYSTEM_RETROFIT_AND_PV,
   SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV,
-  PV
+  PV,
+  SYSTEM_RETROFIT
 ]

costing_package/cost.py

@@ -5,19 +5,18 @@ Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@conco
 Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
 Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
 """
-import datetime
 
 import pandas as pd
 import numpy_financial as npf
 from hub.city_model_structure.building import Building
 from hub.helpers.dictionaries import Dictionaries
-from scripts.costs.configuration import Configuration
-from scripts.costs.capital_costs import CapitalCosts
-from scripts.costs.end_of_life_costs import EndOfLifeCosts
-from scripts.costs.total_maintenance_costs import TotalMaintenanceCosts
-from scripts.costs.total_operational_costs import TotalOperationalCosts
-from scripts.costs.total_operational_incomes import TotalOperationalIncomes
-from scripts.costs.constants import CURRENT_STATUS, SKIN_RETROFIT, SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV
+from costing_package.configuration import Configuration
+from costing_package.capital_costs import CapitalCosts
+from costing_package.end_of_life_costs import EndOfLifeCosts
+from costing_package.total_maintenance_costs import TotalMaintenanceCosts
+from costing_package.total_operational_costs import TotalOperationalCosts
+from costing_package.total_operational_incomes import TotalOperationalIncomes
+from costing_package.constants import CURRENT_STATUS
 import hub.helpers.constants as cte
 
 
@@ -93,10 +92,12 @@ class Cost:
         global_capital_costs['B1010_superstructure']
     )
     df_capital_costs_systems = (
-        global_capital_costs['D3020_heat_and_cooling_generating_systems'] +
-        global_capital_costs['D3040_distribution_systems'] +
-        global_capital_costs['D3050_other_hvac_ahu'] +
-        global_capital_costs['D3060_storage_systems'] +
+        global_capital_costs['D3020_simultaneous_heat_and_cooling_generating_systems'] +
+        global_capital_costs['D3030_heating_systems'] +
+        global_capital_costs['D3040_cooling_systems'] +
+        global_capital_costs['D3050_distribution_systems'] +
+        global_capital_costs['D3060_other_hvac_ahu'] +
+        global_capital_costs['D3070_storage_systems'] +
         global_capital_costs['D40_dhw'] +
         global_capital_costs['D2010_photovoltaic_system']
     )

costing_package/cost_base.py

@@ -8,7 +8,7 @@ Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
 
 from hub.city_model_structure.building import Building
 
-from scripts.costs.configuration import Configuration
+from costing_package.configuration import Configuration
 
 
 class CostBase:

costing_package/end_of_life_costs.py

@@ -9,8 +9,8 @@ import math
 import pandas as pd
 from hub.city_model_structure.building import Building
 
-from scripts.costs.configuration import Configuration
-from scripts.costs.cost_base import CostBase
+from costing_package.configuration import Configuration
+from costing_package.cost_base import CostBase
 
 
 class EndOfLifeCosts(CostBase):

costing_package/total_maintenance_costs.py

@@ -10,8 +10,8 @@ import pandas as pd
 from hub.city_model_structure.building import Building
 import hub.helpers.constants as cte
 
-from scripts.costs.configuration import Configuration
-from scripts.costs.cost_base import CostBase
+from costing_package.configuration import Configuration
+from costing_package.cost_base import CostBase
 
 
 class TotalMaintenanceCosts(CostBase):

costing_package/total_operational_costs.py

@@ -10,9 +10,9 @@ import pandas as pd
 from hub.city_model_structure.building import Building
 import hub.helpers.constants as cte
 
-from scripts.costs.configuration import Configuration
-from scripts.costs.cost_base import CostBase
-from scripts.costs.peak_load import PeakLoad
+from costing_package.configuration import Configuration
+from costing_package.cost_base import CostBase
+from costing_package.peak_load import PeakLoad
 
 
 class TotalOperationalCosts(CostBase):
@@ -196,7 +196,7 @@ class TotalOperationalCosts(CostBase):
         if cooling is not None:
           hourly += cooling[i] / 3600
         if dhw is not None:
-          dhw += dhw[i] / 3600
+          hourly += dhw[i] / 3600
         hourly_fuel_consumption.append(hourly)
     else:
       heating = None

costing_package/total_operational_incomes.py

@@ -10,8 +10,8 @@ import pandas as pd
 from hub.city_model_structure.building import Building
 import hub.helpers.constants as cte
 
-from scripts.costs.configuration import Configuration
-from scripts.costs.cost_base import CostBase
+from costing_package.configuration import Configuration
+from costing_package.cost_base import CostBase
 
 
 class TotalOperationalIncomes(CostBase):
@@ -33,14 +33,12 @@ class TotalOperationalIncomes(CostBase):
       onsite_electricity_production = 0
     else:
       onsite_electricity_production = building.onsite_electrical_production[cte.YEAR][0]
-
     for year in range(1, self._configuration.number_of_years + 1):
       price_increase_electricity = math.pow(1 + self._configuration.electricity_price_index, year)
-      # todo: check the adequate assignation of price. Pilar
-      price_export = archetype.income.electricity_export * cte.WATTS_HOUR_TO_JULES * 1000  # to account for unit change
+      price_export = archetype.income.electricity_export  # to account for unit change
       self._yearly_operational_incomes.loc[year, 'Incomes electricity'] = (
-          onsite_electricity_production * price_export * price_increase_electricity
+        (onsite_electricity_production / 3.6e6) * price_export * price_increase_electricity
       )
 
     self._yearly_operational_incomes.fillna(0, inplace=True)
-    return self._yearly_operational_incomes
+    return self._yearly_operational_incomes

district_heating_network.py

@@ -0,0 +1,111 @@
+from scripts.district_heating_network.directory_manager import DirectoryManager
+import subprocess
+from scripts.ep_run_enrich import energy_plus_workflow
+from hub.imports.geometry_factory import GeometryFactory
+from hub.helpers.dictionaries import Dictionaries
+from hub.imports.construction_factory import ConstructionFactory
+from hub.imports.usage_factory import UsageFactory
+from hub.imports.weather_factory import WeatherFactory
+from hub.imports.results_factory import ResultFactory
+from scripts.energy_system_retrofit_report import EnergySystemRetrofitReport
+from scripts.geojson_creator import process_geojson
+from scripts import random_assignation
+from hub.imports.energy_systems_factory import EnergySystemsFactory
+from scripts.energy_system_sizing import SystemSizing
+from scripts.solar_angles import CitySolarAngles
+from scripts.pv_sizing_and_simulation import PVSizingSimulation
+from scripts.energy_system_retrofit_results import consumption_data, cost_data
+from scripts.energy_system_sizing_and_simulation_factory import EnergySystemsSimulationFactory
+from scripts.costs.cost import Cost
+from scripts.costs.constants import SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT_AND_PV, CURRENT_STATUS
+import hub.helpers.constants as cte
+from hub.exports.exports_factory import ExportsFactory
+from scripts.pv_feasibility import pv_feasibility
+import matplotlib.pyplot as plt
+import numpy as np
+from scripts.district_heating_network.district_heating_network_creator import DistrictHeatingNetworkCreator
+from scripts.district_heating_network.district_heating_factory import DistrictHeatingFactory
+import json
+
+#%% --------------------------------------------------------------------------------------------------------------------
+# Manage File Path
+base_path = "./"
+dir_manager = DirectoryManager(base_path)
+
+# Input files directory
+input_files_path = dir_manager.create_directory('input_files')
+geojson_file_path = input_files_path / 'output_buildings.geojson'
+pipe_data_file = input_files_path / 'pipe_data.json'
+
+# Output files directory
+output_path = dir_manager.create_directory('out_files')
+
+# Subdirectories for output files
+energy_plus_output_path = dir_manager.create_directory('out_files/energy_plus_outputs')
+simulation_results_path = dir_manager.create_directory('out_files/simulation_results')
+sra_output_path = dir_manager.create_directory('out_files/sra_outputs')
+cost_analysis_output_path = dir_manager.create_directory('out_files/cost_analysis')
+
+#%% --------------------------------------------------------------------------------------------------------------------
+# Area Under Study
+location = [45.4934614681437, -73.57982834742518]
+
+#%% --------------------------------------------------------------------------------------------------------------------
+# Create geojson of buildings
+process_geojson(x=location[1], y=location[0], diff=0.001)
+
+#%% --------------------------------------------------------------------------------------------------------------------
+# Create ciry and run energyplus workflow
+city = GeometryFactory(file_type='geojson',
+                       path=geojson_file_path,
+                       height_field='height',
+                       year_of_construction_field='year_of_construction',
+                       function_field='function',
+                       function_to_hub=Dictionaries().montreal_function_to_hub_function).city
+ConstructionFactory('nrcan', city).enrich()
+UsageFactory('nrcan', city).enrich()
+WeatherFactory('epw', city).enrich()
+
+# SRA
+ExportsFactory('sra', city, output_path).export()
+sra_path = (output_path / f'{city.name}_sra.xml').resolve()
+subprocess.run(['sra', str(sra_path)])
+ResultFactory('sra', city, output_path).enrich()
+
+# EP Workflow
+energy_plus_workflow(city, energy_plus_output_path)
+
+#%% --------------------------------------------------------------------------------------------------------------------
+# District Heating Network Creator
+central_plant_locations = [(-73.57812571080625, 45.49499447346277)]  # Add at least one location
+
+roads_file = "./input_files/roads.json"
+
+dhn_creator = DistrictHeatingNetworkCreator(geojson_file_path, roads_file, central_plant_locations)
+
+network_graph = dhn_creator.run()
+
+#%% --------------------------------------------------------------------------------------------------------------------
+# Pipe and pump sizing
+
+with open(pipe_data_file, 'r') as f:
+  pipe_data = json.load(f)
+
+factory = DistrictHeatingFactory(
+  city=city,
+  graph=network_graph,
+  supply_temperature=80 + 273,  # in Kelvin
+  return_temperature=60 + 273,  # in Kelvin
+  simultaneity_factor=0.9
+)
+
+factory.enrich()
+factory.sizing()
+factory.calculate_diameters_and_costs(pipe_data)
+pipe_groups, total_cost = factory.analyze_costs()
+
+# Save the pipe groups with total costs to a CSV file
+factory.save_pipe_groups_to_csv('pipe_groups.csv')
+
+#%% --------------------------------------------------------------------------------------------------------------------
+

energy_system_modelling_package/energy_system_modelling_factories/archetypes/montreal/archetype_cluster_1.py

@@ -0,0 +1,116 @@
+import csv
+
+from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.heat_pump_boiler_tes_heating import \
+  HeatPumpBoilerTesHeating
+from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.heat_pump_cooling import \
+  HeatPumpCooling
+from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.domestic_hot_water_heat_pump_with_tes import \
+  DomesticHotWaterHeatPumpTes
+import hub.helpers.constants as cte
+from hub.helpers.monthly_values import MonthlyValues
+
+
+class ArchetypeCluster1:
+  def __init__(self, building, dt, output_path, csv_output=True):
+    self.building = building
+    self.dt = dt
+    self.output_path = output_path
+    self.csv_output = csv_output
+    self.heating_results, self.building_heating_hourly_consumption = self.heating_system_simulation()
+    self.cooling_results, self.total_cooling_consumption_hourly = self.cooling_system_simulation()
+    self.dhw_results, self.total_dhw_consumption_hourly = self.dhw_system_simulation()
+
+  def heating_system_simulation(self):
+    building_heating_hourly_consumption = []
+    boiler = self.building.energy_systems[1].generation_systems[0]
+    hp = self.building.energy_systems[1].generation_systems[1]
+    tes = self.building.energy_systems[1].generation_systems[0].energy_storage_systems[0]
+    heating_demand_joules = self.building.heating_demand[cte.HOUR]
+    heating_peak_load_watts = self.building.heating_peak_load[cte.YEAR][0]
+    upper_limit_tes_heating = 55
+    outdoor_temperature = self.building.external_temperature[cte.HOUR]
+    results = HeatPumpBoilerTesHeating(hp=hp,
+                                       boiler=boiler,
+                                       tes=tes,
+                                       hourly_heating_demand_joules=heating_demand_joules,
+                                       heating_peak_load_watts=heating_peak_load_watts,
+                                       upper_limit_tes=upper_limit_tes_heating,
+                                       outdoor_temperature=outdoor_temperature,
+                                       dt=self.dt).simulation()
+    number_of_ts = int(cte.HOUR_TO_SECONDS/self.dt)
+    heating_consumption_joules = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in
+                                  results['Total Heating Power Consumption (W)']]
+    heating_consumption = 0
+    for i in range(1, len(heating_consumption_joules)):
+      heating_consumption += heating_consumption_joules[i]
+      if (i - 1) % number_of_ts == 0:
+        building_heating_hourly_consumption.append(heating_consumption)
+        heating_consumption = 0
+    return results, building_heating_hourly_consumption
+
+  def cooling_system_simulation(self):
+    hp = self.building.energy_systems[2].generation_systems[0]
+    cooling_demand_joules = self.building.cooling_demand[cte.HOUR]
+    cooling_peak_load = self.building.cooling_peak_load[cte.YEAR][0]
+    cutoff_temperature = 13
+    outdoor_temperature = self.building.external_temperature[cte.HOUR]
+    results = HeatPumpCooling(hp=hp,
+                              hourly_cooling_demand_joules=cooling_demand_joules,
+                              cooling_peak_load_watts=cooling_peak_load,
+                              cutoff_temperature=cutoff_temperature,
+                              outdoor_temperature=outdoor_temperature,
+                              dt=self.dt).simulation()
+    building_cooling_hourly_consumption = hp.energy_consumption[cte.COOLING][cte.HOUR]
+    return results, building_cooling_hourly_consumption
+
+  def dhw_system_simulation(self):
+    building_dhw_hourly_consumption = []
+    hp = self.building.energy_systems[-1].generation_systems[0]
+    tes = self.building.energy_systems[-1].generation_systems[0].energy_storage_systems[0]
+    dhw_demand_joules = self.building.domestic_hot_water_heat_demand[cte.HOUR]
+    upper_limit_tes = 65
+    outdoor_temperature = self.building.external_temperature[cte.HOUR]
+    results = DomesticHotWaterHeatPumpTes(hp=hp,
+                                          tes=tes,
+                                          hourly_dhw_demand_joules=dhw_demand_joules,
+                                          upper_limit_tes=upper_limit_tes,
+                                          outdoor_temperature=outdoor_temperature,
+                                          dt=self.dt).simulation()
+    number_of_ts = int(cte.HOUR_TO_SECONDS/self.dt)
+    dhw_consumption_joules = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in
+                              results['Total DHW Power Consumption (W)']]
+    dhw_consumption = 0
+    for i in range(1, len(dhw_consumption_joules)):
+      dhw_consumption += dhw_consumption_joules[i]
+      if (i - 1) % number_of_ts == 0:
+        building_dhw_hourly_consumption.append(dhw_consumption)
+        dhw_consumption = 0
+    return results, building_dhw_hourly_consumption
+
+
+  def enrich_building(self):
+    results = self.heating_results | self.cooling_results | self.dhw_results
+    self.building.heating_consumption[cte.HOUR] = self.building_heating_hourly_consumption
+    self.building.heating_consumption[cte.MONTH] = (
+      MonthlyValues.get_total_month(self.building.heating_consumption[cte.HOUR]))
+    self.building.heating_consumption[cte.YEAR] = [sum(self.building.heating_consumption[cte.MONTH])]
+    self.building.cooling_consumption[cte.HOUR] = self.total_cooling_consumption_hourly
+    self.building.cooling_consumption[cte.MONTH] = (
+      MonthlyValues.get_total_month(self.building.cooling_consumption[cte.HOUR]))
+    self.building.cooling_consumption[cte.YEAR] = [sum(self.building.cooling_consumption[cte.MONTH])]
+    self.building.domestic_hot_water_consumption[cte.HOUR] = self.total_dhw_consumption_hourly
+    self.building.domestic_hot_water_consumption[cte.MONTH] = (
+      MonthlyValues.get_total_month(self.building.domestic_hot_water_consumption[cte.HOUR]))
+    self.building.domestic_hot_water_consumption[cte.YEAR] = [
+      sum(self.building.domestic_hot_water_consumption[cte.MONTH])]
+    if self.csv_output:
+      file_name = f'energy_system_simulation_results_{self.building.name}.csv'
+      with open(self.output_path / file_name, 'w', newline='') as csvfile:
+        output_file = csv.writer(csvfile)
+        # Write header
+        output_file.writerow(results.keys())
+        # Write data
+        output_file.writerows(zip(*results.values()))
+
+
+

energy_system_modelling_package/energy_system_modelling_factories/energy_system_sizing_factory.py

@@ -0,0 +1,52 @@
+"""
+EnergySystemSizingSimulationFactory retrieve the energy system archetype sizing and simulation module
+SPDX - License - Identifier: LGPL - 3.0 - or -later
+Copyright © 2024 Concordia CERC group
+Project Coder Saeed Ranjbar saeed.ranjbar@mail.concordia.ca
+"""
+
+from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.peak_load_sizing import \
+  PeakLoadSizing
+from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.heuristic_sizing import \
+  HeuristicSizing
+
+
+class EnergySystemsSizingFactory:
+  """
+  EnergySystemsFactory class
+  """
+
+  def __init__(self, handler, city):
+    self._handler = '_' + handler.lower()
+    self._city = city
+
+  def _peak_load_sizing(self):
+    """
+    Size Energy Systems based on a Load Matching method using the heating, cooling, and dhw peak loads
+    """
+    PeakLoadSizing(self._city).enrich_buildings()
+    self._city.level_of_detail.energy_systems = 1
+    for building in self._city.buildings:
+      building.level_of_detail.energy_systems = 1
+
+  def _heurisitc_sizing(self):
+    """
+    Size Energy Systems using a Single or Multi Objective GA
+    """
+    HeuristicSizing(self._city).enrich_buildings()
+    self._city.level_of_detail.energy_systems = 1
+    for building in self._city.buildings:
+      building.level_of_detail.energy_systems = 1
+
+  def _district_heating_cooling_sizing(self):
+    """
+    Size District Heating and Cooling Network
+    """
+    pass
+
+  def enrich(self):
+    """
+    Enrich the city given to the class using the class given handler
+    :return: None
+    """
+    return getattr(self, self._handler, lambda: None)()

energy_system_modelling_package/energy_system_modelling_factories/hvac_dhw_systems_simulation_models/domestic_hot_water_heat_pump_with_tes.py

@@ -0,0 +1,118 @@
+import hub.helpers.constants as cte
+from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.heat_pump_characteristics import HeatPump
+from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.thermal_storage_tank import StorageTank
+from hub.helpers.monthly_values import MonthlyValues
+
+
+class DomesticHotWaterHeatPumpTes:
+  def __init__(self, hp, tes, hourly_dhw_demand_joules, upper_limit_tes,
+               outdoor_temperature, dt=900):
+    self.hp = hp
+    self.tes = tes
+    self.dhw_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in hourly_dhw_demand_joules]
+    self.upper_limit_tes = upper_limit_tes
+    self.hp_characteristics = HeatPump(self.hp, outdoor_temperature)
+    self.t_out = outdoor_temperature
+    self.dt = dt
+    self.results = {}
+
+  def simulation(self):
+    hp = self.hp
+    tes = self.tes
+    heating_coil_nominal_output = 0
+    if tes.heating_coil_capacity is not None:
+      heating_coil_nominal_output = float(tes.heating_coil_capacity)
+    storage_tank = StorageTank(volume=float(tes.volume),
+                               height=float(tes.height),
+                               material_layers=tes.layers,
+                               heating_coil_capacity=heating_coil_nominal_output)
+
+    hp_delta_t = 8
+    number_of_ts = int(cte.HOUR_TO_SECONDS / self.dt)
+    source_temperature_hourly = self.hp_characteristics.hp_source_temperature()
+    source_temperature = [x for x in source_temperature_hourly for _ in range(number_of_ts)]
+    demand = [x for x in self.dhw_demand for _ in range(number_of_ts)]
+    variable_names = ["t_sup_hp", "t_tank", "m_ch", "m_dis", "q_hp", "q_coil", "hp_cop",
+                      "hp_electricity", "available hot water (m3)", "refill flow rate (kg/s)", "total_consumption"]
+    num_hours = len(demand)
+    variables = {name: [0] * num_hours for name in variable_names}
+    (t_sup_hp, t_tank, m_ch, m_dis, m_refill, q_hp, q_coil, hp_cop, hp_electricity, v_dhw, total_consumption) = \
+        [variables[name] for name in variable_names]
+    freshwater_temperature = 18
+    t_tank[0] = 70
+    for i in range(len(demand) - 1):
+      delta_t_demand = demand[i] * (self.dt / (cte.WATER_DENSITY * cte.WATER_HEAT_CAPACITY *
+                                                        storage_tank.volume))
+      if t_tank[i] < self.upper_limit_tes:
+        q_hp[i] = hp.nominal_heat_output
+      delta_t_hp = q_hp[i] * (self.dt / (cte.WATER_DENSITY * cte.WATER_HEAT_CAPACITY * storage_tank.volume))
+      if demand[i] > 0:
+        dhw_needed = (demand[i] * cte.HOUR_TO_SECONDS) / (cte.WATER_HEAT_CAPACITY * t_tank[i] * cte.WATER_DENSITY)
+        m_dis[i] = dhw_needed * cte.WATER_DENSITY / cte.HOUR_TO_SECONDS
+        m_refill[i] = m_dis[i]
+      delta_t_freshwater = m_refill[i] * (t_tank[i] - freshwater_temperature) * (self.dt / (storage_tank.volume *
+                                                                                            cte.WATER_DENSITY))
+      if t_tank[i] < 60:
+        q_coil[i] = float(storage_tank.heating_coil_capacity)
+      delta_t_coil = q_coil[i] * (self.dt / (cte.WATER_DENSITY * cte.WATER_HEAT_CAPACITY * storage_tank.volume))
+      if q_hp[i] > 0:
+        m_ch[i] = q_hp[i] / (cte.WATER_HEAT_CAPACITY * hp_delta_t)
+        t_sup_hp[i] = (q_hp[i] / (m_ch[i] * cte.WATER_HEAT_CAPACITY)) + t_tank[i]
+      else:
+        m_ch[i] = 0
+        t_sup_hp[i] = t_tank[i]
+      if q_hp[i] > 0:
+        if hp.source_medium == cte.AIR and hp.supply_medium == cte.WATER:
+          hp_cop[i] = self.hp_characteristics.air_to_water_cop(source_temperature[i], t_tank[i],
+                                                               mode=cte.DOMESTIC_HOT_WATER)
+        hp_electricity[i] = q_hp[i] / hp_cop[i]
+      else:
+        hp_cop[i] = 0
+        hp_electricity[i] = 0
+
+      t_tank[i + 1] = t_tank[i] + (delta_t_hp - delta_t_freshwater - delta_t_demand + delta_t_coil)
+      total_consumption[i] = hp_electricity[i] + q_coil[i]
+    tes.temperature = []
+    hp_electricity_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in hp_electricity]
+    heating_coil_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in q_coil]
+    hp_hourly = []
+    coil_hourly = []
+    coil_sum = 0
+    hp_sum = 0
+    for i in range(1, len(demand)):
+      hp_sum += hp_electricity_j[i]
+      coil_sum += heating_coil_j[i]
+      if (i - 1) % number_of_ts == 0:
+        tes.temperature.append(t_tank[i])
+        hp_hourly.append(hp_sum)
+        coil_hourly.append(coil_sum)
+        hp_sum = 0
+        coil_sum = 0
+    hp.energy_consumption[cte.DOMESTIC_HOT_WATER] = {}
+    hp.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR] = hp_hourly
+    hp.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.MONTH] = MonthlyValues.get_total_month(
+      hp.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR])
+    hp.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.YEAR] = [
+      sum(hp.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.MONTH])]
+    if self.tes.heating_coil_capacity is not None:
+      tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER] = {}
+      tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR] = coil_hourly
+      tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER][cte.MONTH] = MonthlyValues.get_total_month(
+        tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR])
+      tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER][cte.YEAR] = [
+        sum(tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER][cte.MONTH])]
+
+    self.results['DHW Demand (W)'] = demand
+    self.results['DHW HP Heat Output (W)'] = q_hp
+    self.results['DHW HP Electricity Consumption (W)'] = hp_electricity
+    self.results['DHW HP Source Temperature'] = source_temperature
+    self.results['DHW HP Supply Temperature'] = t_sup_hp
+    self.results['DHW HP COP'] = hp_cop
+    self.results['DHW TES Heating Coil Heat Output (W)'] = q_coil
+    self.results['DHW TES Temperature'] = t_tank
+    self.results['DHW TES Charging Flow Rate (kg/s)'] = m_ch
+    self.results['DHW Flow Rate (kg/s)'] = m_dis
+    self.results['DHW TES Refill Flow Rate (kg/s)'] = m_refill
+    self.results['Available Water in Tank (m3)'] = v_dhw
+    self.results['Total DHW Power Consumption (W)'] = total_consumption
+    return self.results

energy_system_modelling_package/energy_system_modelling_factories/hvac_dhw_systems_simulation_models/heat_pump_boiler_tes_heating.py

@@ -0,0 +1,166 @@
+import hub.helpers.constants as cte
+from hub.helpers.monthly_values import MonthlyValues
+from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.heat_pump_characteristics import \
+  HeatPump
+from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.thermal_storage_tank import \
+  StorageTank
+
+
+class HeatPumpBoilerTesHeating:
+  def __init__(self, hp, boiler, tes, hourly_heating_demand_joules, heating_peak_load_watts, upper_limit_tes,
+               outdoor_temperature, dt=900):
+    self.hp = hp
+    self.boiler = boiler
+    self.tes = tes
+    self.heating_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in hourly_heating_demand_joules]
+    self.heating_peak_load = heating_peak_load_watts
+    self.upper_limit_tes = upper_limit_tes
+    self.hp_characteristics = HeatPump(self.hp, outdoor_temperature)
+    self.t_out = outdoor_temperature
+    self.dt = dt
+    self.results = {}
+
+  def simulation(self):
+    hp, boiler, tes = self.hp, self.boiler, self.tes
+    heating_coil_nominal_output = 0
+    if tes.heating_coil_capacity is not None:
+      heating_coil_nominal_output = float(tes.heating_coil_capacity)
+    storage_tank = StorageTank(volume=float(tes.volume),
+                               height=float(tes.height),
+                               material_layers=tes.layers,
+                               heating_coil_capacity=heating_coil_nominal_output)
+    number_of_ts = int(cte.HOUR_TO_SECONDS / self.dt)
+    demand = [0] + [x for x in self.heating_demand for _ in range(number_of_ts)]
+    t_out = [0] + [x for x in self.t_out for _ in range(number_of_ts)]
+    source_temperature_hourly = self.hp_characteristics.hp_source_temperature()
+    source_temperature = [0] + [x for x in source_temperature_hourly for _ in range(number_of_ts)]
+    variable_names = ["t_sup_hp", "t_tank", "t_ret", "m_ch", "m_dis", "q_hp", "q_boiler", "hp_cop",
+                      "hp_electricity", "boiler_gas_consumption", "t_sup_boiler", "boiler_energy_consumption",
+                      "heating_consumption", "heating_coil_output", "total_heating_energy_consumption"]
+    num_hours = len(demand)
+    variables = {name: [0] * num_hours for name in variable_names}
+    (t_sup_hp, t_tank, t_ret, m_ch, m_dis, q_hp, q_boiler, hp_cop,
+     hp_electricity, boiler_fuel_consumption, t_sup_boiler, boiler_energy_consumption, heating_consumption, q_coil,
+     total_consumption) = [variables[name] for name in variable_names]
+    t_tank[0] = self.upper_limit_tes
+    hp_delta_t = 5
+    # storage temperature prediction
+    for i in range(len(demand) - 1):
+      t_tank[i + 1] = storage_tank.calculate_space_heating_fully_mixed(charging_flow_rate=m_ch[i],
+                                                                       discharging_flow_rate=m_dis[i],
+                                                                       supply_temperature=t_sup_boiler[i],
+                                                                       return_temperature=t_ret[i],
+                                                                       current_tank_temperature=t_tank[i],
+                                                                       heat_generator_input=q_coil[i],
+                                                                       ambient_temperature=t_out[i],
+                                                                       dt=self.dt)
+      # hp operation
+      if t_tank[i + 1] < 40:
+        q_hp[i + 1] = hp.nominal_heat_output
+        m_ch[i + 1] = q_hp[i + 1] / (cte.WATER_HEAT_CAPACITY * hp_delta_t)
+        t_sup_hp[i + 1] = (q_hp[i + 1] / (m_ch[i + 1] * cte.WATER_HEAT_CAPACITY)) + t_tank[i + 1]
+      elif 40 <= t_tank[i + 1] < self.upper_limit_tes and q_hp[i] == 0:
+        q_hp[i + 1] = 0
+        m_ch[i + 1] = 0
+        t_sup_hp[i + 1] = t_tank[i + 1]
+      elif 40 <= t_tank[i + 1] < self.upper_limit_tes and q_hp[i] > 0:
+        q_hp[i + 1] = hp.nominal_heat_output
+        m_ch[i + 1] = q_hp[i + 1] / (cte.WATER_HEAT_CAPACITY * hp_delta_t)
+        t_sup_hp[i + 1] = (q_hp[i + 1] / (m_ch[i + 1] * cte.WATER_HEAT_CAPACITY)) + t_tank[i + 1]
+      else:
+        q_hp[i + 1], m_ch[i + 1], t_sup_hp[i + 1] = 0, 0, t_tank[i + 1]
+      if q_hp[i + 1] > 0:
+        if hp.source_medium == cte.AIR and self.hp.supply_medium == cte.WATER:
+          hp_cop[i + 1] = self.hp_characteristics.air_to_water_cop(source_temperature[i + 1], t_tank[i + 1], mode=cte.HEATING)
+          hp_electricity[i + 1] = q_hp[i + 1] / hp_cop[i + 1]
+      else:
+        hp_cop[i + 1] = 0
+        hp_electricity[i + 1] = 0
+      # boiler operation
+      if q_hp[i + 1] > 0:
+        if t_sup_hp[i + 1] < 45:
+          q_boiler[i + 1] = boiler.nominal_heat_output
+        elif demand[i + 1] > 0.5 * self.heating_peak_load / self.dt:
+          q_boiler[i + 1] = 0.5 * boiler.nominal_heat_output
+        boiler_energy_consumption[i + 1] = q_boiler[i + 1] / float(boiler.heat_efficiency)
+        if boiler.fuel_type == cte.ELECTRICITY:
+          boiler_fuel_consumption[i + 1] = boiler_energy_consumption[i + 1]
+        else:
+            # TODO: Other fuels should be considered
+          boiler_fuel_consumption[i + 1] = (q_boiler[i + 1] * self.dt) / (
+              float(boiler.heat_efficiency) * cte.NATURAL_GAS_LHV)
+        t_sup_boiler[i + 1] = t_sup_hp[i + 1] + (q_boiler[i + 1] / (m_ch[i + 1] * cte.WATER_HEAT_CAPACITY))
+      # heating coil operation
+      if t_tank[i + 1] < 35:
+        q_coil[i + 1] = heating_coil_nominal_output
+      # storage discharging
+      if demand[i + 1] == 0:
+        m_dis[i + 1] = 0
+        t_ret[i + 1] = t_tank[i + 1]
+      else:
+        if demand[i + 1] > 0.5 * self.heating_peak_load:
+          factor = 8
+        else:
+          factor = 4
+        m_dis[i + 1] = self.heating_peak_load / (cte.WATER_HEAT_CAPACITY * factor)
+        t_ret[i + 1] = t_tank[i + 1] - demand[i + 1] / (m_dis[i + 1] * cte.WATER_HEAT_CAPACITY)
+      # total consumption
+      total_consumption[i + 1] = hp_electricity[i + 1] + boiler_energy_consumption[i + 1] + q_coil[i + 1]
+    tes.temperature = []
+    hp_electricity_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in hp_electricity]
+    boiler_consumption_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in boiler_energy_consumption]
+    heating_coil_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in q_coil]
+    hp_hourly = []
+    boiler_hourly = []
+    coil_hourly = []
+    boiler_sum = 0
+    hp_sum = 0
+    coil_sum = 0
+    for i in range(1, len(demand)):
+      hp_sum += hp_electricity_j[i]
+      boiler_sum += boiler_consumption_j[i]
+      coil_sum += heating_coil_j[i]
+      if (i - 1) % number_of_ts == 0:
+        tes.temperature.append(t_tank[i])
+        hp_hourly.append(hp_sum)
+        boiler_hourly.append(boiler_sum)
+        coil_hourly.append(coil_sum)
+        hp_sum = 0
+        boiler_sum = 0
+        coil_sum = 0
+    hp.energy_consumption[cte.HEATING] = {}
+    hp.energy_consumption[cte.HEATING][cte.HOUR] = hp_hourly
+    hp.energy_consumption[cte.HEATING][cte.MONTH] = MonthlyValues.get_total_month(
+      hp.energy_consumption[cte.HEATING][cte.HOUR])
+    hp.energy_consumption[cte.HEATING][cte.YEAR] = [
+      sum(hp.energy_consumption[cte.HEATING][cte.MONTH])]
+    boiler.energy_consumption[cte.HEATING] = {}
+    boiler.energy_consumption[cte.HEATING][cte.HOUR] = boiler_hourly
+    boiler.energy_consumption[cte.HEATING][cte.MONTH] = MonthlyValues.get_total_month(
+      boiler.energy_consumption[cte.HEATING][cte.HOUR])
+    boiler.energy_consumption[cte.HEATING][cte.YEAR] = [
+      sum(boiler.energy_consumption[cte.HEATING][cte.MONTH])]
+    if tes.heating_coil_capacity is not None:
+      tes.heating_coil_energy_consumption[cte.HEATING] = {}
+      tes.heating_coil_energy_consumption[cte.HEATING][cte.HOUR] = coil_hourly
+      tes.heating_coil_energy_consumption[cte.HEATING][cte.MONTH] = MonthlyValues.get_total_month(
+        tes.heating_coil_energy_consumption[cte.HEATING][cte.HOUR])
+      tes.heating_coil_energy_consumption[cte.HEATING][cte.YEAR] = [
+        sum(tes.heating_coil_energy_consumption[cte.HEATING][cte.MONTH])]
+    self.results['Heating Demand (W)'] = demand
+    self.results['HP Heat Output (W)'] = q_hp
+    self.results['HP Source Temperature'] = source_temperature
+    self.results['HP Supply Temperature'] = t_sup_hp
+    self.results['HP COP'] = hp_cop
+    self.results['HP Electricity Consumption (W)'] = hp_electricity
+    self.results['Boiler Heat Output (W)'] = q_boiler
+    self.results['Boiler Power Consumption (W)'] = boiler_energy_consumption
+    self.results['Boiler Supply Temperature'] = t_sup_boiler
+    self.results['Boiler Fuel Consumption'] = boiler_fuel_consumption
+    self.results['TES Temperature'] = t_tank
+    self.results['Heating Coil heat input'] = q_coil
+    self.results['TES Charging Flow Rate (kg/s)'] = m_ch
+    self.results['TES Discharge Flow Rate (kg/s)'] = m_dis
+    self.results['Heating Loop Return Temperature'] = t_ret
+    self.results['Total Heating Power Consumption (W)'] = total_consumption
+    return self.results

energy_system_modelling_package/energy_system_modelling_factories/hvac_dhw_systems_simulation_models/heat_pump_characteristics.py

@@ -0,0 +1,54 @@
+import hub.helpers.constants as cte
+
+
+class HeatPump:
+  def __init__(self, hp, t_out):
+    self.hp = hp
+    self.t_out = t_out
+
+  def hp_source_temperature(self):
+    if self.hp.source_medium == cte.AIR:
+      self.hp.source_temperature = self.t_out
+    elif self.hp.source_medium == cte.GROUND:
+      average_air_temperature = sum(self.t_out) / len(self.t_out)
+      self.hp.source_temperature = [average_air_temperature + 10] * len(self.t_out)
+    elif self.hp.source_medium == cte.WATER:
+      self.hp.source_temperature = [15] * len(self.t_out)
+    return self.hp.source_temperature
+
+  def air_to_water_cop(self, source_temperature, inlet_water_temperature, mode=cte.HEATING):
+    cop_coefficient = 1
+    t_inlet_water_fahrenheit = 1.8 * inlet_water_temperature + 32
+    t_source_fahrenheit = 1.8 * source_temperature + 32
+    if mode == cte.HEATING:
+      if self.hp.heat_efficiency_curve is not None:
+        cop_curve_coefficients = [float(coefficient) for coefficient in self.hp.heat_efficiency_curve.coefficients]
+        cop_coefficient = (1 / (cop_curve_coefficients[0] +
+                                cop_curve_coefficients[1] * t_inlet_water_fahrenheit +
+                                cop_curve_coefficients[2] * t_inlet_water_fahrenheit ** 2 +
+                                cop_curve_coefficients[3] * t_source_fahrenheit +
+                                cop_curve_coefficients[4] * t_source_fahrenheit ** 2 +
+                                cop_curve_coefficients[5] * t_inlet_water_fahrenheit * t_source_fahrenheit))
+      hp_efficiency = float(self.hp.heat_efficiency)
+    elif mode == cte.COOLING:
+      if self.hp.cooling_efficiency_curve is not None:
+        cop_curve_coefficients = [float(coefficient) for coefficient in self.hp.cooling_efficiency_curve.coefficients]
+        cop_coefficient = (1 / (cop_curve_coefficients[0] +
+                                cop_curve_coefficients[1] * t_inlet_water_fahrenheit +
+                                cop_curve_coefficients[2] * t_inlet_water_fahrenheit ** 2 +
+                                cop_curve_coefficients[3] * t_source_fahrenheit +
+                                cop_curve_coefficients[4] * t_source_fahrenheit ** 2 +
+                                cop_curve_coefficients[5] * t_inlet_water_fahrenheit * t_source_fahrenheit))
+      hp_efficiency = float(self.hp.cooling_efficiency)
+    else:
+      if self.hp.heat_efficiency_curve is not None:
+        cop_curve_coefficients = [float(coefficient) for coefficient in self.hp.heat_efficiency_curve.coefficients]
+        cop_coefficient = (cop_curve_coefficients[0] +
+                           cop_curve_coefficients[1] * source_temperature +
+                           cop_curve_coefficients[2] * source_temperature ** 2 +
+                           cop_curve_coefficients[3] * inlet_water_temperature +
+                           cop_curve_coefficients[4] * inlet_water_temperature ** 2 +
+                           cop_curve_coefficients[5] * inlet_water_temperature * source_temperature)
+      hp_efficiency = float(self.hp.heat_efficiency)
+    hp_cop = cop_coefficient * hp_efficiency
+    return hp_cop

energy_system_modelling_package/energy_system_modelling_factories/hvac_dhw_systems_simulation_models/heat_pump_cooling.py

@@ -0,0 +1,86 @@
+import hub.helpers.constants as cte
+from hub.helpers.monthly_values import MonthlyValues
+from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.heat_pump_characteristics import HeatPump
+
+
+class HeatPumpCooling:
+  def __init__(self, hp, hourly_cooling_demand_joules, cooling_peak_load_watts, cutoff_temperature, outdoor_temperature,
+               dt=900):
+    self.hp = hp
+    self.cooling_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in hourly_cooling_demand_joules]
+    self.cooling_peak_load = cooling_peak_load_watts
+    self.cutoff_temperature = cutoff_temperature
+    self.dt = dt
+    self.results = {}
+    self.heat_pump_characteristics = HeatPump(self.hp, outdoor_temperature)
+
+  def simulation(self):
+    source_temperature_hourly = self.heat_pump_characteristics.hp_source_temperature()
+    cooling_efficiency = float(self.hp.cooling_efficiency)
+    number_of_ts = int(cte.HOUR_TO_SECONDS / self.dt)
+    demand = [0] + [x for x in self.cooling_demand for _ in range(number_of_ts)]
+    source_temperature = [0] + [x for x in source_temperature_hourly for _ in range(number_of_ts)]
+    variable_names = ["t_sup_hp", "t_ret", "m", "q_hp", "hp_electricity", "hp_cop"]
+    num_hours = len(demand)
+    variables = {name: [0] * num_hours for name in variable_names}
+    (t_sup_hp, t_ret, m, q_hp, hp_electricity, hp_cop) = [variables[name] for name in variable_names]
+    t_ret[0] = self.cutoff_temperature
+
+    for i in range(1, len(demand)):
+      if demand[i] > 0.15 * self.cooling_peak_load:
+        m[i] = self.hp.nominal_cooling_output / (cte.WATER_HEAT_CAPACITY * 5)
+        if t_ret[i - 1] >= self.cutoff_temperature:
+          if demand[i] < 0.25 * self.cooling_peak_load:
+            q_hp[i] = 0.25 * self.hp.nominal_cooling_output
+          elif demand[i] < 0.5 * self.cooling_peak_load:
+            q_hp[i] = 0.5 * self.hp.nominal_cooling_output
+          else:
+            q_hp[i] = self.hp.nominal_cooling_output
+          t_sup_hp[i] = t_ret[i - 1] - q_hp[i] / (m[i] * cte.WATER_HEAT_CAPACITY)
+        else:
+          q_hp[i] = 0
+          t_sup_hp[i] = t_ret[i - 1]
+        if m[i] == 0:
+          t_ret[i] = t_sup_hp[i]
+        else:
+          t_ret[i] = t_sup_hp[i] + demand[i] / (m[i] * cte.WATER_HEAT_CAPACITY)
+      else:
+        m[i] = 0
+        q_hp[i] = 0
+        t_sup_hp[i] = t_ret[i - 1]
+        t_ret[i] = t_ret[i - 1]
+      if q_hp[i] > 0:
+        if self.hp.source_medium == cte.AIR and self.hp.supply_medium == cte.WATER:
+          hp_cop[i] = self.heat_pump_characteristics.air_to_water_cop(source_temperature[i], t_ret[i],
+                                                                      mode=cte.COOLING)
+          hp_electricity[i] = q_hp[i] / cooling_efficiency
+      else:
+        hp_cop[i] = 0
+        hp_electricity[i] = 0
+    hp_electricity_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in hp_electricity]
+    hp_hourly = []
+    hp_sum = 0
+    for i in range(1, len(demand)):
+      hp_sum += hp_electricity_j[i]
+      if (i - 1) % number_of_ts == 0:
+        hp_hourly.append(hp_sum)
+        hp_sum = 0
+    self.hp.energy_consumption[cte.COOLING] = {}
+    self.hp.energy_consumption[cte.COOLING][cte.HOUR] = hp_hourly
+    self.hp.energy_consumption[cte.COOLING][cte.MONTH] = MonthlyValues.get_total_month(
+      self.hp.energy_consumption[cte.COOLING][cte.HOUR])
+    self.hp.energy_consumption[cte.COOLING][cte.YEAR] = [
+      sum(self.hp.energy_consumption[cte.COOLING][cte.MONTH])]
+    self.results['Cooling Demand (W)'] = demand
+    self.results['HP Cooling Output (W)'] = q_hp
+    self.results['HP Source Temperature'] = source_temperature
+    self.results['HP Cooling Supply Temperature'] = t_sup_hp
+    self.results['HP Cooling COP'] = hp_cop
+    self.results['HP Electricity Consumption'] = hp_electricity
+    self.results['Cooling Loop Flow Rate (kg/s)'] = m
+    self.results['Cooling Loop Return Temperature'] = t_ret
+    return self.results
+
+
+
+

energy_system_modelling_package/energy_system_modelling_factories/hvac_dhw_systems_simulation_models/thermal_storage_tank.py

@@ -0,0 +1,47 @@
+import math
+
+import hub.helpers.constants as cte
+
+
+class StorageTank:
+  """
+  Calculation of the temperature inside a hot water storage tank in the next time step
+  """
+
+  def __init__(self, volume, height, material_layers, heating_coil_capacity, stratification_layer=1):
+    self.volume = volume
+    self.height = height
+    self.materials = material_layers
+    self.number_of_vertical_layers = stratification_layer
+    self.heating_coil_capacity = heating_coil_capacity
+
+  def heat_loss_coefficient(self):
+    r_tot = sum(float(layer.thickness) / float(layer.material.conductivity) for layer in
+                self.materials)
+    u_tot = 1 / r_tot
+    d = math.sqrt((4 * self.volume) / (math.pi * self.height))
+    a_side = math.pi * d * self.height
+    a_top = math.pi * d ** 2 / 4
+    if self.number_of_vertical_layers == 1:
+      ua = u_tot * (2 * a_top + a_side)
+      return ua
+    else:
+      ua_side = u_tot * a_side
+      ua_top_bottom = u_tot * (a_top + a_side)
+      return ua_side, ua_top_bottom
+
+
+  def calculate_space_heating_fully_mixed(self, charging_flow_rate, discharging_flow_rate, supply_temperature,
+                                          return_temperature,
+                                          current_tank_temperature, heat_generator_input, ambient_temperature, dt):
+    ua = self.heat_loss_coefficient()
+    t_tank = (current_tank_temperature +
+              (charging_flow_rate * (supply_temperature - current_tank_temperature) +
+               (ua * (ambient_temperature - current_tank_temperature)) / cte.WATER_HEAT_CAPACITY -
+               discharging_flow_rate * (current_tank_temperature - return_temperature) +
+               heat_generator_input / cte.WATER_HEAT_CAPACITY) * (dt / (cte.WATER_DENSITY * self.volume)))
+    return t_tank
+
+  def calculate_dhw_fully_mixed(self, charging_flow_rate, discharging_flow_rate, supply_temperature, return_temperature,
+                                current_tank_temperature, heat_generator_input, ambient_temperature, dt):
+    pass

energy_system_modelling_package/energy_system_modelling_factories/montreal_energy_system_archetype_modelling_factory.py

@@ -1,16 +1,14 @@
 """
 EnergySystemSizingSimulationFactory retrieve the energy system archetype sizing and simulation module
 SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2022 Concordia CERC group
+Copyright © 2024 Concordia CERC group
 Project Coder Saeed Ranjbar saeed.ranjbar@mail.concordia.ca
 """
 
-from scripts.system_simulation_models.archetype13 import Archetype13
-from scripts.system_simulation_models.archetype13_stratified_tes import Archetype13Stratified
-from scripts.system_simulation_models.archetype1 import Archetype1
+from energy_system_modelling_package.energy_system_modelling_factories.archetypes.montreal.archetype_cluster_1 import ArchetypeCluster1
 
 
-class EnergySystemsSimulationFactory:
+class MontrealEnergySystemArchetypesSimulationFactory:
   """
   EnergySystemsFactory class
   """
@@ -20,19 +18,12 @@ class EnergySystemsSimulationFactory:
     self._handler = '_' + handler.lower()
     self._building = building
 
-  def _archetype1(self):
+  def _archetype_cluster_1(self):
     """
     Enrich the city by using the sizing and simulation model developed for archetype13 of montreal_future_systems
     """
-    Archetype1(self._building, self._output_path).enrich_buildings()
-    self._building.level_of_detail.energy_systems = 2
-    self._building.level_of_detail.energy_systems = 2
-
-  def _archetype13(self):
-    """
-    Enrich the city by using the sizing and simulation model developed for archetype13 of montreal_future_systems
-    """
-    Archetype13(self._building, self._output_path).enrich_buildings()
+    dt = 900
+    ArchetypeCluster1(self._building, dt, self._output_path, csv_output=True).enrich_building()
     self._building.level_of_detail.energy_systems = 2
     self._building.level_of_detail.energy_systems = 2
 

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/electricity_demand_calculator.py

@@ -0,0 +1,75 @@
+import hub.helpers.constants as cte
+class HourlyElectricityDemand:
+  def __init__(self, building):
+    self.building = building
+
+  def calculate(self):
+    hourly_electricity_consumption = []
+    energy_systems = self.building.energy_systems
+    appliance = self.building.appliances_electrical_demand[cte.HOUR] if self.building.appliances_electrical_demand else 0
+    lighting = self.building.lighting_electrical_demand[cte.HOUR] if self.building.lighting_electrical_demand else 0
+    elec_heating = 0
+    elec_cooling = 0
+    elec_dhw = 0
+    if cte.HEATING in self.building.energy_consumption_breakdown[cte.ELECTRICITY]:
+      elec_heating = 1
+    if cte.COOLING in self.building.energy_consumption_breakdown[cte.ELECTRICITY]:
+      elec_cooling = 1
+    if cte.DOMESTIC_HOT_WATER in self.building.energy_consumption_breakdown[cte.ELECTRICITY]:
+      elec_dhw = 1
+    heating = None
+    cooling = None
+    dhw = None
+
+    if elec_heating == 1:
+      for energy_system in energy_systems:
+        if cte.HEATING in energy_system.demand_types:
+          for generation_system in energy_system.generation_systems:
+            if generation_system.fuel_type == cte.ELECTRICITY:
+              if cte.HEATING in generation_system.energy_consumption:
+                heating = generation_system.energy_consumption[cte.HEATING][cte.HOUR]
+              else:
+                if len(energy_system.generation_systems) > 1:
+                  heating = [x / 2 for x in self.building.heating_consumption[cte.HOUR]]
+                else:
+                  heating = self.building.heating_consumption[cte.HOUR]
+
+    if elec_dhw == 1:
+      for energy_system in energy_systems:
+        if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
+          for generation_system in energy_system.generation_systems:
+            if generation_system.fuel_type == cte.ELECTRICITY:
+              if cte.DOMESTIC_HOT_WATER in generation_system.energy_consumption:
+                dhw = generation_system.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR]
+              else:
+                if len(energy_system.generation_systems) > 1:
+                  dhw = [x / 2 for x in self.building.domestic_hot_water_consumption[cte.HOUR]]
+                else:
+                  dhw = self.building.domestic_hot_water_consumption[cte.HOUR]
+
+    if elec_cooling == 1:
+      for energy_system in energy_systems:
+        if cte.COOLING in energy_system.demand_types:
+          for generation_system in energy_system.generation_systems:
+            if cte.COOLING in generation_system.energy_consumption:
+              cooling = generation_system.energy_consumption[cte.COOLING][cte.HOUR]
+            else:
+              if len(energy_system.generation_systems) > 1:
+                cooling = [x / 2 for x in self.building.cooling_consumption[cte.HOUR]]
+              else:
+                cooling = self.building.cooling_consumption[cte.HOUR]
+
+    for i in range(8760):
+      hourly = 0
+      if isinstance(appliance, list):
+        hourly += appliance[i]
+      if isinstance(lighting, list):
+        hourly += lighting[i]
+      if heating is not None:
+        hourly += heating[i]
+      if cooling is not None:
+        hourly += cooling[i]
+      if dhw is not None:
+        hourly += dhw[i]
+      hourly_electricity_consumption.append(hourly)
+    return hourly_electricity_consumption

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/pv_system_assessment.py

@@ -0,0 +1,225 @@
+import math
+import csv
+import hub.helpers.constants as cte
+from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.electricity_demand_calculator import \
+  HourlyElectricityDemand
+from hub.catalog_factories.energy_systems_catalog_factory import EnergySystemsCatalogFactory
+from hub.helpers.monthly_values import MonthlyValues
+
+
+class PvSystemAssessment:
+  def __init__(self, building=None, pv_system=None, battery=None, electricity_demand=None, tilt_angle=None,
+               solar_angles=None, pv_installation_type=None, simulation_model_type=None, module_model_name=None,
+               inverter_efficiency=None, system_catalogue_handler=None, roof_percentage_coverage=None,
+               facade_coverage_percentage=None, csv_output=False, output_path=None):
+    """
+    :param building:
+    :param tilt_angle:
+    :param solar_angles:
+    :param simulation_model_type:
+    :param module_model_name:
+    :param inverter_efficiency:
+    :param system_catalogue_handler:
+    :param roof_percentage_coverage:
+    :param facade_coverage_percentage:
+    """
+    self.building = building
+    self.electricity_demand = electricity_demand
+    self.tilt_angle = tilt_angle
+    self.solar_angles = solar_angles
+    self.pv_installation_type = pv_installation_type
+    self.simulation_model_type = simulation_model_type
+    self.module_model_name = module_model_name
+    self.inverter_efficiency = inverter_efficiency
+    self.system_catalogue_handler = system_catalogue_handler
+    self.roof_percentage_coverage = roof_percentage_coverage
+    self.facade_coverage_percentage = facade_coverage_percentage
+    self.pv_hourly_generation = None
+    self.t_cell = None
+    self.results = {}
+    self.csv_output = csv_output
+    self.output_path = output_path
+    if pv_system is not None:
+      self.pv_system = pv_system
+    else:
+      for energy_system in self.building.energy_systems:
+        for generation_system in energy_system.generation_systems:
+          if generation_system.system_type == cte.PHOTOVOLTAIC:
+            self.pv_system = generation_system
+    if battery is not None:
+      self.battery = battery
+    else:
+      for energy_system in self.building.energy_systems:
+        for generation_system in energy_system.generation_systems:
+          if generation_system.system_type == cte.PHOTOVOLTAIC and generation_system.energy_storage_systems is not None:
+            for storage_system in generation_system.energy_storage_systems:
+              if storage_system.type_energy_stored == cte.ELECTRICAL:
+                self.battery = storage_system
+
+  @staticmethod
+  def explicit_model(pv_system, inverter_efficiency, number_of_panels, irradiance, outdoor_temperature):
+    inverter_efficiency = inverter_efficiency
+    stc_power = float(pv_system.standard_test_condition_maximum_power)
+    stc_irradiance = float(pv_system.standard_test_condition_radiation)
+    cell_temperature_coefficient = float(pv_system.cell_temperature_coefficient) / 100 if (
+        pv_system.cell_temperature_coefficient is not None) else None
+    stc_t_cell = float(pv_system.standard_test_condition_cell_temperature)
+    nominal_condition_irradiance = float(pv_system.nominal_radiation)
+    nominal_condition_cell_temperature = float(pv_system.nominal_cell_temperature)
+    nominal_t_out = float(pv_system.nominal_ambient_temperature)
+    g_i = irradiance
+    t_out = outdoor_temperature
+    t_cell = []
+    pv_output = []
+    for i in range(len(g_i)):
+      t_cell.append((t_out[i] + (g_i[i] / nominal_condition_irradiance) *
+                     (nominal_condition_cell_temperature - nominal_t_out)))
+      pv_output.append((inverter_efficiency * number_of_panels * (stc_power * (g_i[i] / stc_irradiance) *
+                                                                  (1 - cell_temperature_coefficient *
+                                                                   (t_cell[i] - stc_t_cell)))))
+    return pv_output
+
+  def rooftop_sizing(self):
+    pv_system = self.pv_system
+    if self.module_model_name is not None:
+      self.system_assignation()
+    # System Sizing
+    module_width = float(pv_system.width)
+    module_height = float(pv_system.height)
+    roof_area = 0
+    for roof in self.building.roofs:
+      roof_area += roof.perimeter_area
+    pv_module_area = module_width * module_height
+    available_roof = (self.roof_percentage_coverage * roof_area)
+    # Inter-Row Spacing
+    winter_solstice = self.solar_angles[(self.solar_angles['AST'].dt.month == 12) &
+                                        (self.solar_angles['AST'].dt.day == 21) &
+                                        (self.solar_angles['AST'].dt.hour == 12)]
+    solar_altitude = winter_solstice['solar altitude'].values[0]
+    solar_azimuth = winter_solstice['solar azimuth'].values[0]
+    distance = ((module_height * math.sin(math.radians(self.tilt_angle)) * abs(
+      math.cos(math.radians(solar_azimuth)))) / math.tan(math.radians(solar_altitude)))
+    distance = float(format(distance, '.2f'))
+    # Calculation of the number of panels
+    space_dimension = math.sqrt(available_roof)
+    space_dimension = float(format(space_dimension, '.2f'))
+    panels_per_row = math.ceil(space_dimension / module_width)
+    number_of_rows = math.ceil(space_dimension / distance)
+    total_number_of_panels = panels_per_row * number_of_rows
+    total_pv_area = total_number_of_panels * pv_module_area
+    self.building.roofs[0].installed_solar_collector_area = total_pv_area
+    return panels_per_row, number_of_rows
+
+  def system_assignation(self):
+    generation_units_catalogue = EnergySystemsCatalogFactory(self.system_catalogue_handler).catalog
+    catalog_pv_generation_equipments = [component for component in
+                                        generation_units_catalogue.entries('generation_equipments') if
+                                        component.system_type == 'photovoltaic']
+    selected_pv_module = None
+    for pv_module in catalog_pv_generation_equipments:
+      if self.module_model_name == pv_module.model_name:
+        selected_pv_module = pv_module
+    if selected_pv_module is None:
+      raise ValueError("No PV module with the provided model name exists in the catalogue")
+    for energy_system in self.building.energy_systems:
+      for idx, generation_system in enumerate(energy_system.generation_systems):
+        if generation_system.system_type == cte.PHOTOVOLTAIC:
+          new_system = selected_pv_module
+        # Preserve attributes that exist in the original but not in the new system
+          for attr in dir(generation_system):
+            # Skip private attributes and methods
+            if not attr.startswith('__') and not callable(getattr(generation_system, attr)):
+              if not hasattr(new_system, attr):
+                setattr(new_system, attr, getattr(generation_system, attr))
+        # Replace the old generation system with the new one
+          energy_system.generation_systems[idx] = new_system
+
+  def grid_tied_system(self):
+    if self.electricity_demand is not None:
+      electricity_demand = self.electricity_demand
+    else:
+      electricity_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in
+                                          HourlyElectricityDemand(self.building).calculate()]
+    rooftop_pv_output = [0] * 8760
+    facade_pv_output = [0] * 8760
+    rooftop_number_of_panels = 0
+    if 'rooftop' in self.pv_installation_type.lower():
+      np, ns = self.rooftop_sizing()
+      if self.simulation_model_type == 'explicit':
+        rooftop_number_of_panels = np * ns
+        rooftop_pv_output = self.explicit_model(pv_system=self.pv_system,
+                                                inverter_efficiency=self.inverter_efficiency,
+                                                number_of_panels=rooftop_number_of_panels,
+                                                irradiance=self.building.roofs[0].global_irradiance_tilted[
+                                                  cte.HOUR],
+                                                outdoor_temperature=self.building.external_temperature[
+                                                  cte.HOUR])
+
+    total_hourly_pv_output = [rooftop_pv_output[i] + facade_pv_output[i] for i in range(8760)]
+    imported_electricity = [0] * 8760
+    exported_electricity = [0] * 8760
+    for i in range(len(electricity_demand)):
+      transfer = total_hourly_pv_output[i] - electricity_demand[i]
+      if transfer > 0:
+        exported_electricity[i] = transfer
+      else:
+        imported_electricity[i] = abs(transfer)
+
+    results = {'building_name': self.building.name,
+               'total_floor_area_m2': self.building.thermal_zones_from_internal_zones[0].total_floor_area,
+               'roof_area_m2': self.building.roofs[0].perimeter_area, 'rooftop_panels': rooftop_number_of_panels,
+               'rooftop_panels_area_m2': self.building.roofs[0].installed_solar_collector_area,
+               'yearly_rooftop_ghi_kW/m2': self.building.roofs[0].global_irradiance[cte.YEAR][0] / 1000,
+               f'yearly_rooftop_tilted_radiation_{self.tilt_angle}_degree_kW/m2':
+                 self.building.roofs[0].global_irradiance_tilted[cte.YEAR][0] / 1000,
+               'yearly_rooftop_pv_production_kWh': sum(rooftop_pv_output) / 1000,
+               'yearly_total_pv_production_kWh': sum(total_hourly_pv_output) / 1000,
+               'specific_pv_production_kWh/kWp': sum(rooftop_pv_output) / (
+                   float(self.pv_system.standard_test_condition_maximum_power) * rooftop_number_of_panels),
+               'hourly_rooftop_poa_irradiance_W/m2': self.building.roofs[0].global_irradiance_tilted[cte.HOUR],
+               'hourly_rooftop_pv_output_W': rooftop_pv_output, 'T_out': self.building.external_temperature[cte.HOUR],
+               'building_electricity_demand_W': electricity_demand,
+               'total_hourly_pv_system_output_W': total_hourly_pv_output, 'import_from_grid_W': imported_electricity,
+               'export_to_grid_W': exported_electricity}
+    return results
+
+  def enrich(self):
+    system_archetype_name = self.building.energy_systems_archetype_name
+    archetype_name = '_'.join(system_archetype_name.lower().split())
+    if 'grid_tied' in archetype_name:
+      self.results = self.grid_tied_system()
+    hourly_pv_output = self.results['total_hourly_pv_system_output_W']
+    self.building.onsite_electrical_production[cte.HOUR] = hourly_pv_output
+    self.building.onsite_electrical_production[cte.MONTH] = MonthlyValues.get_total_month(hourly_pv_output)
+    self.building.onsite_electrical_production[cte.YEAR] = [sum(hourly_pv_output)]
+    if self.csv_output:
+      self.save_to_csv(self.results, self.output_path, f'{self.building.name}_pv_system_analysis.csv')
+
+  @staticmethod
+  def save_to_csv(data, output_path, filename='rooftop_system_results.csv'):
+    # Separate keys based on whether their values are single values or lists
+    single_value_keys = [key for key, value in data.items() if not isinstance(value, list)]
+    list_value_keys = [key for key, value in data.items() if isinstance(value, list)]
+
+    # Check if all lists have the same length
+    list_lengths = [len(data[key]) for key in list_value_keys]
+    if not all(length == list_lengths[0] for length in list_lengths):
+      raise ValueError("All lists in the dictionary must have the same length")
+
+    # Get the length of list values (assuming all lists are of the same length, e.g., 8760 for hourly data)
+    num_rows = list_lengths[0] if list_value_keys else 1
+
+    # Open the CSV file for writing
+    with open(output_path / filename, mode='w', newline='') as csv_file:
+      writer = csv.writer(csv_file)
+    # Write single-value data as a header section
+      for key in single_value_keys:
+        writer.writerow([key, data[key]])
+    # Write an empty row for separation
+      writer.writerow([])
+    # Write the header for the list values
+      writer.writerow(list_value_keys)
+    # Write each row for the lists
+      for i in range(num_rows):
+        row = [data[key][i] for key in list_value_keys]
+        writer.writerow(row)

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/solar_calculator.py

@@ -0,0 +1,221 @@
+import math
+import pandas as pd
+from datetime import datetime
+import hub.helpers.constants as cte
+from hub.helpers.monthly_values import MonthlyValues
+
+
+class SolarCalculator:
+  def __init__(self, city, tilt_angle, surface_azimuth_angle, standard_meridian=-75,
+               solar_constant=1366.1, maximum_clearness_index=1, min_cos_zenith=0.065, maximum_zenith_angle=87):
+    """
+    A class to calculate the solar angles and solar irradiance on a tilted surface in the City
+    :param city: An object from the City class -> City
+    :param tilt_angle: tilt angle of surface -> float
+    :param surface_azimuth_angle: The orientation of the surface. 0 is North -> float
+    :param standard_meridian: A standard meridian is the meridian whose mean solar time is the basis of the time of day
+    observed in a time zone -> float
+    :param solar_constant: The amount of energy received by a given area one astronomical unit away from the Sun. It is
+    constant and must not be changed
+    :param maximum_clearness_index: This is used to calculate the diffuse fraction of the solar irradiance -> float
+    :param min_cos_zenith: This is needed to avoid unrealistic values in tilted irradiance calculations -> float
+    :param maximum_zenith_angle: This is needed to avoid negative values in tilted irradiance calculations -> float
+    """
+    self.city = city
+    self.location_latitude = city.latitude
+    self.location_longitude = city.longitude
+    self.location_latitude_rad = math.radians(self.location_latitude)
+    self.surface_azimuth_angle = surface_azimuth_angle
+    self.surface_azimuth_rad = math.radians(surface_azimuth_angle)
+    self.tilt_angle = tilt_angle
+    self.tilt_angle_rad = math.radians(tilt_angle)
+    self.standard_meridian = standard_meridian
+    self.longitude_correction = (self.location_longitude - standard_meridian) * 4
+    self.solar_constant = solar_constant
+    self.maximum_clearness_index = maximum_clearness_index
+    self.min_cos_zenith = min_cos_zenith
+    self.maximum_zenith_angle = maximum_zenith_angle
+    timezone_offset = int(-standard_meridian / 15)
+    self.timezone = f'Etc/GMT{"+" if timezone_offset < 0 else "-"}{abs(timezone_offset)}'
+    self.eot = []
+    self.ast = []
+    self.hour_angles = []
+    self.declinations = []
+    self.solar_altitudes = []
+    self.solar_azimuths = []
+    self.zeniths = []
+    self.incidents = []
+    self.i_on = []
+    self.i_oh = []
+    self.times = pd.date_range(start='2023-01-01', end='2023-12-31 23:00', freq='h', tz=self.timezone)
+    self.solar_angles = pd.DataFrame(index=self.times)
+    self.day_of_year = self.solar_angles.index.dayofyear
+
+  def solar_time(self, datetime_val, day_of_year):
+    b = (day_of_year - 81) * 2 * math.pi / 364
+    eot = 9.87 * math.sin(2 * b) - 7.53 * math.cos(b) - 1.5 * math.sin(b)
+    self.eot.append(eot)
+
+    # Calculate Local Solar Time (LST)
+    lst_hour = datetime_val.hour
+    lst_minute = datetime_val.minute
+    lst_second = datetime_val.second
+    lst = lst_hour + lst_minute / 60 + lst_second / 3600
+
+    # Calculate Apparent Solar Time (AST) in decimal hours
+    ast_decimal = lst + eot / 60 + self.longitude_correction / 60
+    ast_hours = int(ast_decimal) % 24  # Adjust hours to fit within 0–23 range
+    ast_minutes = round((ast_decimal - ast_hours) * 60)
+
+    # Ensure ast_minutes is within valid range
+    if ast_minutes == 60:
+      ast_hours += 1
+      ast_minutes = 0
+    elif ast_minutes < 0:
+      ast_minutes = 0
+    ast_time = datetime(year=datetime_val.year, month=datetime_val.month, day=datetime_val.day,
+                        hour=ast_hours, minute=ast_minutes)
+    self.ast.append(ast_time)
+    return ast_time
+
+  def declination_angle(self, day_of_year):
+    declination = 23.45 * math.sin(math.radians(360 / 365 * (284 + day_of_year)))
+    declination_radian = math.radians(declination)
+    self.declinations.append(declination)
+    return declination_radian
+
+  def hour_angle(self, ast_time):
+    hour_angle = ((ast_time.hour * 60 + ast_time.minute) - 720) / 4
+    hour_angle_radian = math.radians(hour_angle)
+    self.hour_angles.append(hour_angle)
+    return hour_angle_radian
+
+  def solar_altitude(self, declination_radian, hour_angle_radian):
+    solar_altitude_radians = math.asin(math.cos(self.location_latitude_rad) * math.cos(declination_radian) *
+                                       math.cos(hour_angle_radian) + math.sin(self.location_latitude_rad) *
+                                       math.sin(declination_radian))
+    solar_altitude = math.degrees(solar_altitude_radians)
+    self.solar_altitudes.append(solar_altitude)
+    return solar_altitude_radians
+
+  def zenith(self, solar_altitude_radians):
+    solar_altitude = math.degrees(solar_altitude_radians)
+    zenith_degree = 90 - solar_altitude
+    zenith_radian = math.radians(zenith_degree)
+    self.zeniths.append(zenith_degree)
+    return zenith_radian
+
+  def solar_azimuth_analytical(self, hourangle, declination, zenith):
+    numer = (math.cos(zenith) * math.sin(self.location_latitude_rad) - math.sin(declination))
+    denom = (math.sin(zenith) * math.cos(self.location_latitude_rad))
+    if math.isclose(denom, 0.0, abs_tol=1e-8):
+      cos_azi = 1.0
+    else:
+      cos_azi = numer / denom
+
+    cos_azi = max(-1.0, min(1.0, cos_azi))
+
+    sign_ha = math.copysign(1, hourangle)
+    solar_azimuth_radians = sign_ha * math.acos(cos_azi) + math.pi
+    solar_azimuth_degrees = math.degrees(solar_azimuth_radians)
+    self.solar_azimuths.append(solar_azimuth_degrees)
+    return solar_azimuth_radians
+
+  def incident_angle(self, solar_altitude_radians, solar_azimuth_radians):
+    incident_radian = math.acos(math.cos(solar_altitude_radians) *
+                                math.cos(abs(solar_azimuth_radians - self.surface_azimuth_rad)) *
+                                math.sin(self.tilt_angle_rad) + math.sin(solar_altitude_radians) *
+                                math.cos(self.tilt_angle_rad))
+    incident_angle_degrees = math.degrees(incident_radian)
+    self.incidents.append(incident_angle_degrees)
+    return incident_radian
+
+  def dni_extra(self, day_of_year, zenith_radian):
+    i_on = self.solar_constant * (1 + 0.033 * math.cos(math.radians(360 * day_of_year / 365)))
+    i_oh = i_on * max(math.cos(zenith_radian), self.min_cos_zenith)
+    self.i_on.append(i_on)
+    self.i_oh.append(i_oh)
+    return i_on, i_oh
+
+  def clearness_index(self, ghi, i_oh):
+    k_t = ghi / i_oh
+    k_t = max(0, k_t)
+    k_t = min(self.maximum_clearness_index, k_t)
+    return k_t
+
+  def diffuse_fraction(self, k_t, zenith):
+    if k_t <= 0.22:
+      fraction_diffuse = 1 - 0.09 * k_t
+    elif k_t <= 0.8:
+      fraction_diffuse = (0.9511 - 0.1604 * k_t + 4.388 * k_t ** 2 - 16.638 * k_t ** 3 + 12.336 * k_t ** 4)
+    else:
+      fraction_diffuse = 0.165
+    if zenith > self.maximum_zenith_angle:
+      fraction_diffuse = 1
+    return fraction_diffuse
+
+  def radiation_components_horizontal(self, ghi, fraction_diffuse, zenith):
+    diffuse_horizontal = ghi * fraction_diffuse
+    dni = (ghi - diffuse_horizontal) / math.cos(math.radians(zenith))
+    if zenith > self.maximum_zenith_angle or dni < 0:
+      dni = 0
+    return diffuse_horizontal, dni
+
+  def radiation_components_tilted(self, diffuse_horizontal, dni, incident_angle):
+    beam_tilted = dni * math.cos(math.radians(incident_angle))
+    beam_tilted = max(beam_tilted, 0)
+    diffuse_tilted = diffuse_horizontal * ((1 + math.cos(math.radians(self.tilt_angle))) / 2)
+    total_radiation_tilted = beam_tilted + diffuse_tilted
+    return total_radiation_tilted
+
+  def solar_angles_calculator(self, csv_output=False):
+    for i in range(len(self.times)):
+      datetime_val = self.times[i]
+      day_of_year = self.day_of_year[i]
+      declination_radians = self.declination_angle(day_of_year)
+      ast_time = self.solar_time(datetime_val, day_of_year)
+      hour_angle_radians = self.hour_angle(ast_time)
+      solar_altitude_radians = self.solar_altitude(declination_radians, hour_angle_radians)
+      zenith_radians = self.zenith(solar_altitude_radians)
+      solar_azimuth_radians = self.solar_azimuth_analytical(hour_angle_radians, declination_radians, zenith_radians)
+      self.incident_angle(solar_altitude_radians, solar_azimuth_radians)
+      self.dni_extra(day_of_year=day_of_year, zenith_radian=zenith_radians)
+    self.solar_angles['DateTime'] = self.times
+    self.solar_angles['AST'] = self.ast
+    self.solar_angles['hour angle'] = self.hour_angles
+    self.solar_angles['eot'] = self.eot
+    self.solar_angles['declination angle'] = self.declinations
+    self.solar_angles['solar altitude'] = self.solar_altitudes
+    self.solar_angles['zenith'] = self.zeniths
+    self.solar_angles['solar azimuth'] = self.solar_azimuths
+    self.solar_angles['incident angle'] = self.incidents
+    self.solar_angles['extraterrestrial normal radiation (Wh/m2)'] = self.i_on
+    self.solar_angles['extraterrestrial radiation on horizontal (Wh/m2)'] = self.i_oh
+    if csv_output:
+      self.solar_angles.to_csv('solar_angles_new.csv')
+
+  def tilted_irradiance_calculator(self):
+    if self.solar_angles.empty:
+      self.solar_angles_calculator()
+    for building in self.city.buildings:
+      hourly_tilted_irradiance = []
+      roof_ghi = building.roofs[0].global_irradiance[cte.HOUR]
+      for i in range(len(roof_ghi)):
+        k_t = self.clearness_index(ghi=roof_ghi[i], i_oh=self.i_oh[i])
+        fraction_diffuse = self.diffuse_fraction(k_t, self.zeniths[i])
+        diffuse_horizontal, dni = self.radiation_components_horizontal(ghi=roof_ghi[i],
+                                                                       fraction_diffuse=fraction_diffuse,
+                                                                       zenith=self.zeniths[i])
+        hourly_tilted_irradiance.append(int(self.radiation_components_tilted(diffuse_horizontal=diffuse_horizontal,
+                                                                             dni=dni,
+                                                                             incident_angle=self.incidents[i])))
+
+      building.roofs[0].global_irradiance_tilted[cte.HOUR] = hourly_tilted_irradiance
+      building.roofs[0].global_irradiance_tilted[cte.MONTH] = (MonthlyValues.get_total_month(
+        building.roofs[0].global_irradiance_tilted[cte.HOUR]))
+      building.roofs[0].global_irradiance_tilted[cte.YEAR] = [sum(building.roofs[0].global_irradiance_tilted[cte.MONTH])]
+
+
+
+
+

energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/heuristic_sizing.py

@@ -0,0 +1,6 @@
+class HeuristicSizing:
+  def __init__(self, city):
+    pass
+
+  def enrich_buildings(self):
+    pass

energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/peak_load_sizing.py

@@ -0,0 +1,99 @@
+import hub.helpers.constants as cte
+
+
+class PeakLoadSizing:
+  def __init__(self, city, default_primary_unit_percentage=0.7, storage_peak_coverage=3):
+    self.city = city
+    self.default_primary_unit_percentage = default_primary_unit_percentage
+    self.storage_peak_coverage = storage_peak_coverage
+
+  def enrich_buildings(self):
+    total_demand = 0
+    for building in self.city.buildings:
+      energy_systems = building.energy_systems
+      for energy_system in energy_systems:
+        if cte.HEATING in energy_system.demand_types:
+          if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
+            total_demand = [(building.heating_demand[cte.HOUR][i] +
+                             building.domestic_hot_water_heat_demand[cte.HOUR][i]) / cte.WATTS_HOUR_TO_JULES
+                            for i in range(len(building.heating_demand[cte.HOUR]))]
+          else:
+            total_demand = building.heating_peak_load[cte.YEAR]
+          design_load = max(total_demand)
+          self.allocate_capacity(energy_system, design_load, cte.HEATING, self.default_primary_unit_percentage)
+          if cte.COOLING in energy_system.demand_types:
+            cooling_design_load = building.cooling_peak_load[cte.YEAR][0]
+            self.allocate_capacity(energy_system, cooling_design_load, cte.COOLING,
+                                   self.default_primary_unit_percentage)
+
+        elif cte.COOLING in energy_system.demand_types:
+          design_load = building.cooling_peak_load[cte.YEAR][0]
+          self.allocate_capacity(energy_system, design_load, cte.COOLING, self.default_primary_unit_percentage)
+        elif cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
+          design_load = building.domestic_hot_water_peak_load[cte.YEAR][0]
+          self.allocate_capacity(energy_system, design_load, cte.DOMESTIC_HOT_WATER,
+                                 self.default_primary_unit_percentage)
+
+        for generation_system in energy_system.generation_systems:
+          storage_systems = generation_system.energy_storage_systems
+          if storage_systems is not None:
+            if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
+              operation_range = 10
+            else:
+              operation_range = 20
+            for storage_system in storage_systems:
+              if storage_system.type_energy_stored == cte.THERMAL:
+                self.tes_sizing(storage_system, max(total_demand), self.storage_peak_coverage, operation_range)
+
+  def allocate_capacity(self, energy_system, design_load, demand_type, default_primary_unit_percentage):
+    if len(energy_system.generation_systems) == 1:
+      # If there's only one generation system, it gets the full design load.
+      if demand_type == cte.HEATING or demand_type == cte.DOMESTIC_HOT_WATER:
+        energy_system.generation_systems[0].nominal_heat_output = design_load
+      elif demand_type == cte.COOLING:
+        energy_system.generation_systems[0].nominal_cooling_output = design_load
+    else:
+      cooling_equipments_number = 0
+      # Distribute the load among generation systems.
+      max_efficiency = 0
+      main_generation_unit = None
+      for generation_system in energy_system.generation_systems:
+        if demand_type == cte.HEATING or demand_type == cte.DOMESTIC_HOT_WATER:
+          if max_efficiency < float(generation_system.heat_efficiency):
+            max_efficiency = float(generation_system.heat_efficiency)
+            main_generation_unit = generation_system
+        elif demand_type == cte.COOLING and generation_system.fuel_type == cte.ELECTRICITY:
+          cooling_equipments_number += 1
+          if max_efficiency < float(generation_system.cooling_efficiency):
+            max_efficiency = float(generation_system.heat_efficiency)
+            main_generation_unit = generation_system
+
+      for generation_system in energy_system.generation_systems:
+        if generation_system.system_type == main_generation_unit.system_type:
+          if demand_type == cte.HEATING or demand_type == cte.DOMESTIC_HOT_WATER:
+            generation_system.nominal_heat_output = round(default_primary_unit_percentage * design_load)
+          elif demand_type == cte.COOLING and cooling_equipments_number > 1:
+            generation_system.nominal_cooling_output = round(default_primary_unit_percentage * design_load)
+          else:
+            generation_system.nominal_cooling_output = design_load
+        else:
+          if demand_type == cte.HEATING or demand_type == cte.DOMESTIC_HOT_WATER:
+            generation_system.nominal_heat_output = round(((1 - default_primary_unit_percentage) * design_load /
+                                                     (len(energy_system.generation_systems) - 1)))
+          elif demand_type == cte.COOLING and cooling_equipments_number > 1:
+            generation_system.nominal_cooling_output = round(((1 - default_primary_unit_percentage) * design_load /
+                                                        (len(energy_system.generation_systems) - 1)))
+
+
+  @staticmethod
+  def tes_sizing(storage, peak_load, coverage, operational_temperature_range):
+    storage.volume = round((peak_load * coverage * cte.WATTS_HOUR_TO_JULES) /
+                           (cte.WATER_HEAT_CAPACITY * cte.WATER_DENSITY * operational_temperature_range))
+
+  @staticmethod
+  def cooling_equipments(energy_system):
+    counter = 0
+    for generation_system in energy_system.generation_systems:
+      if generation_system.fuel_type == cte.ELECTRICITY:
+        counter += 1
+    return counter

energy_system_modelling_package/energy_system_retrofit/energy_system_retrofit_report.py

@@ -0,0 +1,596 @@
+import os
+import hub.helpers.constants as cte
+import matplotlib.pyplot as plt
+from matplotlib import cm
+from energy_system_modelling_package.report_creation import LatexReport
+from matplotlib.ticker import MaxNLocator
+import numpy as np
+from pathlib import Path
+import glob
+
+
+class EnergySystemRetrofitReport:
+  def __init__(self, city, output_path, retrofit_scenario, current_status_energy_consumption_data,
+               retrofitted_energy_consumption_data, current_status_lcc_data, retrofitted_lcc_data):
+    self.city = city
+    self.current_status_data = current_status_energy_consumption_data
+    self.retrofitted_data = retrofitted_energy_consumption_data
+    self.current_status_lcc = current_status_lcc_data
+    self.retrofitted_lcc = retrofitted_lcc_data
+    self.output_path = output_path
+    self.content = []
+    self.retrofit_scenario = retrofit_scenario
+    self.report = LatexReport('energy_system_retrofit_report',
+                              'Energy System Retrofit Report', self.retrofit_scenario, output_path)
+    self.system_schemas_path = (Path(__file__).parent.parent / 'hub' / 'data' / 'energy_systems' / 'schemas')
+    self.charts_path = Path(output_path) / 'charts'
+    self.charts_path.mkdir(parents=True, exist_ok=True)
+    files = glob.glob(f'{self.charts_path}/*')
+    for file in files:
+      os.remove(file)
+
+  def building_energy_info(self):
+    table_data = [
+      ["Building Name", "Year of Construction", "function", "Yearly Heating Demand (MWh)",
+       "Yearly Cooling Demand (MWh)", "Yearly DHW Demand (MWh)", "Yearly Electricity Demand (MWh)"]
+    ]
+    intensity_table_data = [["Building Name", "Total Floor Area $m^2$", "Heating Demand Intensity kWh/ $m^2$",
+                             "Cooling Demand Intensity kWh/ $m^2$", "Electricity Intensity kWh/ $m^2$"]]
+    peak_load_data = [["Building Name", "Heating Peak Load (kW)", "Cooling Peak Load (kW)",
+                       "Domestic Hot Water Peak Load (kW)"]]
+
+    for building in self.city.buildings:
+      total_floor_area = 0
+      for zone in building.thermal_zones_from_internal_zones:
+        total_floor_area += zone.total_floor_area
+      building_data = [
+        building.name,
+        str(building.year_of_construction),
+        building.function,
+        str(format(building.heating_demand[cte.YEAR][0] / 3.6e9, '.2f')),
+        str(format(building.cooling_demand[cte.YEAR][0] / 3.6e9, '.2f')),
+        str(format(building.domestic_hot_water_heat_demand[cte.YEAR][0] / 3.6e9, '.2f')),
+        str(format(
+          (building.lighting_electrical_demand[cte.YEAR][0] + building.appliances_electrical_demand[cte.YEAR][0])
+          / 3.6e9, '.2f')),
+      ]
+      intensity_data = [
+        building.name,
+        str(format(total_floor_area, '.2f')),
+        str(format(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area), '.2f')),
+        str(format(building.cooling_demand[cte.YEAR][0] / (3.6e6 * total_floor_area), '.2f')),
+        str(format(
+          (building.lighting_electrical_demand[cte.YEAR][0] + building.appliances_electrical_demand[cte.YEAR][0]) /
+          (3.6e6 * total_floor_area), '.2f'))
+      ]
+      peak_data = [
+        building.name,
+        str(format(building.heating_peak_load[cte.YEAR][0] / 1000, '.2f')),
+        str(format(building.cooling_peak_load[cte.YEAR][0] / 1000, '.2f')),
+        str(format(
+          (building.lighting_electrical_demand[cte.YEAR][0] + building.appliances_electrical_demand[cte.YEAR][0]) /
+          (3.6e6 * total_floor_area), '.2f'))
+      ]
+      table_data.append(building_data)
+      intensity_table_data.append(intensity_data)
+      peak_load_data.append(peak_data)
+
+    self.report.add_table(table_data, caption='Buildings Energy Consumption Data')
+    self.report.add_table(intensity_table_data, caption='Buildings Energy Use Intensity Data')
+    self.report.add_table(peak_load_data, caption='Buildings Peak Load Data')
+
+  def plot_monthly_energy_demands(self, data, file_name, title):
+    # Data preparation
+    months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
+    demands = {
+      'Heating': ('heating', '#2196f3'),
+      'Cooling': ('cooling', '#ff5a5f'),
+      'DHW': ('dhw', '#4caf50'),
+      'Electricity': ('lighting_appliance', '#ffc107')
+    }
+
+    # Helper function for plotting
+    def plot_bar_chart(ax, demand_type, color, ylabel, title):
+      values = data[demand_type]
+      ax.bar(months, values, color=color, width=0.6, zorder=2)
+      ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
+      ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)
+      ax.set_ylabel(ylabel, fontsize=14, labelpad=10)
+      ax.set_title(title, fontsize=14, weight='bold', alpha=.8, pad=40)
+      ax.xaxis.set_major_locator(MaxNLocator(integer=True))
+      ax.yaxis.set_major_locator(MaxNLocator(integer=True))
+      ax.set_xticks(np.arange(len(months)))
+      ax.set_xticklabels(months, rotation=45, ha='right')
+      ax.bar_label(ax.containers[0], padding=3, color='black', fontsize=12, rotation=90)
+      ax.spines[['top', 'left', 'bottom']].set_visible(False)
+      ax.spines['right'].set_linewidth(1.1)
+      average_value = np.mean(values)
+      ax.axhline(y=average_value, color='grey', linewidth=2, linestyle='--')
+      ax.text(len(months) - 1, average_value, f'Average = {average_value:.1f} kWh', ha='right', va='bottom',
+              color='grey')
+
+    # Plotting
+    fig, axs = plt.subplots(4, 1, figsize=(20, 16), dpi=96)
+    fig.suptitle(title, fontsize=16, weight='bold', alpha=.8)
+
+    plot_bar_chart(axs[0], 'heating', demands['Heating'][1], 'Heating Demand (kWh)', 'Monthly Heating Demand')
+    plot_bar_chart(axs[1], 'cooling', demands['Cooling'][1], 'Cooling Demand (kWh)', 'Monthly Cooling Demand')
+    plot_bar_chart(axs[2], 'dhw', demands['DHW'][1], 'DHW Demand (kWh)', 'Monthly DHW Demand')
+    plot_bar_chart(axs[3], 'lighting_appliance', demands['Electricity'][1], 'Electricity Demand (kWh)',
+                   'Monthly Electricity Demand')
+
+    # Set a white background
+    fig.patch.set_facecolor('white')
+
+    # Adjust the margins around the plot area
+    plt.subplots_adjust(left=0.05, right=0.95, top=0.9, bottom=0.1, hspace=0.5)
+
+    # Save the plot
+    chart_path = self.charts_path / f'{file_name}.png'
+    plt.savefig(chart_path, bbox_inches='tight')
+    plt.close()
+
+    return chart_path
+
+  def plot_monthly_energy_consumption(self, data, file_name, title):
+    # Data preparation
+    months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
+    consumptions = {
+      'Heating': ('heating', '#2196f3', 'Heating Consumption (kWh)', 'Monthly Energy Consumption for Heating'),
+      'Cooling': ('cooling', '#ff5a5f', 'Cooling Consumption (kWh)', 'Monthly Energy Consumption for Cooling'),
+      'DHW': ('dhw', '#4caf50', 'DHW Consumption (kWh)', 'Monthly DHW Consumption')
+    }
+
+    # Helper function for plotting
+    def plot_bar_chart(ax, consumption_type, color, ylabel, title):
+      values = data[consumption_type]
+      ax.bar(months, values, color=color, width=0.6, zorder=2)
+      ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
+      ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)
+      ax.set_xlabel('Month', fontsize=12, labelpad=10)
+      ax.set_ylabel(ylabel, fontsize=14, labelpad=10)
+      ax.set_title(title, fontsize=14, weight='bold', alpha=.8, pad=40)
+      ax.xaxis.set_major_locator(MaxNLocator(integer=True))
+      ax.yaxis.set_major_locator(MaxNLocator(integer=True))
+      ax.set_xticks(np.arange(len(months)))
+      ax.set_xticklabels(months, rotation=45, ha='right')
+      ax.bar_label(ax.containers[0], padding=3, color='black', fontsize=12, rotation=90)
+      ax.spines[['top', 'left', 'bottom']].set_visible(False)
+      ax.spines['right'].set_linewidth(1.1)
+      average_value = np.mean(values)
+      ax.axhline(y=average_value, color='grey', linewidth=2, linestyle='--')
+      ax.text(len(months) - 1, average_value, f'Average = {average_value:.1f} kWh', ha='right', va='bottom',
+              color='grey')
+
+    # Plotting
+    fig, axs = plt.subplots(3, 1, figsize=(20, 15), dpi=96)
+    fig.suptitle(title, fontsize=16, weight='bold', alpha=.8)
+
+    plot_bar_chart(axs[0], 'heating', consumptions['Heating'][1], consumptions['Heating'][2],
+                   consumptions['Heating'][3])
+    plot_bar_chart(axs[1], 'cooling', consumptions['Cooling'][1], consumptions['Cooling'][2],
+                   consumptions['Cooling'][3])
+    plot_bar_chart(axs[2], 'dhw', consumptions['DHW'][1], consumptions['DHW'][2], consumptions['DHW'][3])
+
+    # Set a white background
+    fig.patch.set_facecolor('white')
+
+    # Adjust the margins around the plot area
+    plt.subplots_adjust(left=0.05, right=0.95, top=0.9, bottom=0.1, wspace=0.3, hspace=0.5)
+
+    # Save the plot
+    chart_path = self.charts_path / f'{file_name}.png'
+    plt.savefig(chart_path, bbox_inches='tight')
+    plt.close()
+
+    return chart_path
+
+  def fuel_consumption_breakdown(self, file_name, data):
+    fuel_consumption_breakdown = {}
+    for building in self.city.buildings:
+      for key, breakdown in data[f'{building.name}']['energy_consumption_breakdown'].items():
+        if key not in fuel_consumption_breakdown:
+          fuel_consumption_breakdown[key] = {sector: 0 for sector in breakdown}
+        for sector, value in breakdown.items():
+          if sector in fuel_consumption_breakdown[key]:
+            fuel_consumption_breakdown[key][sector] += value / 3.6e6
+          else:
+            fuel_consumption_breakdown[key][sector] = value / 3.6e6
+
+    plt.style.use('ggplot')
+    num_keys = len(fuel_consumption_breakdown)
+    fig, axs = plt.subplots(1 if num_keys <= 2 else num_keys, min(num_keys, 2), figsize=(12, 5))
+    axs = axs if num_keys > 1 else [axs]  # Ensure axs is always iterable
+
+    for i, (fuel, breakdown) in enumerate(fuel_consumption_breakdown.items()):
+      labels = breakdown.keys()
+      values = breakdown.values()
+      colors = cm.get_cmap('tab20c', len(labels))
+      ax = axs[i] if num_keys > 1 else axs[0]
+      ax.pie(values, labels=labels,
+             autopct=lambda pct: f"{pct:.1f}%\n({pct / 100 * sum(values):.2f})",
+             startangle=90, colors=[colors(j) for j in range(len(labels))])
+      ax.set_title(f'{fuel} Consumption Breakdown')
+
+    plt.suptitle('City Energy Consumption Breakdown', fontsize=16, fontweight='bold')
+    plt.tight_layout(rect=[0, 0, 1, 0.95])  # Adjust layout to fit the suptitle
+
+    chart_path = self.charts_path / f'{file_name}.png'
+    plt.savefig(chart_path, dpi=300)
+    plt.close()
+    return chart_path
+
+  def energy_system_archetype_schematic(self):
+    energy_system_archetypes = {}
+    for building in self.city.buildings:
+      if building.energy_systems_archetype_name not in energy_system_archetypes:
+        energy_system_archetypes[building.energy_systems_archetype_name] = [building.name]
+      else:
+        energy_system_archetypes[building.energy_systems_archetype_name].append(building.name)
+
+    text = ""
+    items = ""
+    for archetype, buildings in energy_system_archetypes.items():
+      buildings_str = ", ".join(buildings)
+      text += f"Figure 4 shows the schematic of the proposed energy system for buildings {buildings_str}.\n"
+      if archetype in ['PV+4Pipe+DHW', 'PV+ASHP+GasBoiler+TES']:
+        text += "This energy system archetype is formed of the following systems: \par"
+        items = ['Rooftop Photovoltaic System: The rooftop PV system is tied to the grid and in case there is surplus '
+                 'energy, it is sold to Hydro-Quebec through their Net-Meterin program.',
+                 '4-Pipe HVAC System: This systems includes a 4-pipe heat pump capable of generating heat and cooling '
+                 'at the same time, a natural gas boiler as the auxiliary heating system, and a hot water storage tank.'
+                 'The temperature inside the tank is kept between 40-55 C. The cooling demand is totally supplied by '
+                 'the heat pump unit.',
+                 'Domestic Hot Water Heat Pump System: This system is in charge of supplying domestic hot water demand.'
+                 'The heat pump is connected to a thermal storage tank with electric resistance heating coil inside it.'
+                 ' The temperature inside the tank should always remain above 60 C.']
+
+      self.report.add_text(text)
+      self.report.add_itemize(items=items)
+      schema_path = self.system_schemas_path / f'{archetype}.jpg'
+      self.report.add_image(str(schema_path).replace('\\', '/'),
+                            f'Proposed energy system for buildings {buildings_str}')
+
+  def plot_monthly_radiation(self):
+    months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
+    monthly_roof_radiation = []
+    for i in range(len(months)):
+      tilted_radiation = 0
+      for building in self.city.buildings:
+        tilted_radiation += (building.roofs[0].global_irradiance_tilted[cte.MONTH][i] /
+                             (cte.WATTS_HOUR_TO_JULES * 1000))
+      monthly_roof_radiation.append(tilted_radiation)
+
+    def plot_bar_chart(ax, months, values, color, ylabel, title):
+      ax.bar(months, values, color=color, width=0.6, zorder=2)
+      ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
+      ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)
+      ax.set_xlabel('Month', fontsize=12, labelpad=10)
+      ax.set_ylabel(ylabel, fontsize=14, labelpad=10)
+      ax.set_title(title, fontsize=14, weight='bold', alpha=.8, pad=40)
+      ax.xaxis.set_major_locator(MaxNLocator(integer=True))
+      ax.yaxis.set_major_locator(MaxNLocator(integer=True))
+      ax.set_xticks(np.arange(len(months)))
+      ax.set_xticklabels(months, rotation=45, ha='right')
+      ax.bar_label(ax.containers[0], padding=3, color='black', fontsize=12, rotation=90)
+      ax.spines[['top', 'left', 'bottom']].set_visible(False)
+      ax.spines['right'].set_linewidth(1.1)
+      average_value = np.mean(values)
+      ax.axhline(y=average_value, color='grey', linewidth=2, linestyle='--')
+      ax.text(len(months) - 1, average_value, f'Average = {average_value:.1f} kWh', ha='right', va='bottom',
+              color='grey')
+
+    # Plotting the bar chart
+    fig, ax = plt.subplots(figsize=(15, 8), dpi=96)
+    plot_bar_chart(ax, months, monthly_roof_radiation, '#ffc107', 'Tilted Roof Radiation (kWh / m2)',
+                   'Monthly Tilted Roof Radiation')
+
+    # Set a white background
+    fig.patch.set_facecolor('white')
+
+    # Adjust the margins around the plot area
+    plt.subplots_adjust(left=0.1, right=0.95, top=0.9, bottom=0.1)
+
+    # Save the plot
+    chart_path = self.charts_path / 'monthly_tilted_roof_radiation.png'
+    plt.savefig(chart_path, bbox_inches='tight')
+    plt.close()
+    return chart_path
+
+  def energy_consumption_comparison(self, current_status_data, retrofitted_data, file_name, title):
+    months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
+    consumptions = {
+      'Heating': ('heating', '#2196f3', 'Heating Consumption (kWh)', 'Monthly Energy Consumption for Heating'),
+      'Cooling': ('cooling', '#ff5a5f', 'Cooling Consumption (kWh)', 'Monthly Energy Consumption for Cooling'),
+      'DHW': ('dhw', '#4caf50', 'DHW Consumption (kWh)', 'Monthly DHW Consumption')
+    }
+
+    # Helper function for plotting
+    def plot_double_bar_chart(ax, consumption_type, color, ylabel, title):
+      current_values = current_status_data[consumption_type]
+      retrofitted_values = retrofitted_data[consumption_type]
+      bar_width = 0.35
+      index = np.arange(len(months))
+
+      ax.bar(index, current_values, bar_width, label='Current Status', color=color, alpha=0.7, zorder=2)
+      ax.bar(index + bar_width, retrofitted_values, bar_width, label='Retrofitted', color=color, hatch='/', zorder=2)
+
+      ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
+      ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)
+      ax.set_xlabel('Month', fontsize=12, labelpad=10)
+      ax.set_ylabel(ylabel, fontsize=14, labelpad=10)
+      ax.set_title(title, fontsize=14, weight='bold', alpha=.8, pad=40)
+      ax.set_xticks(index + bar_width / 2)
+      ax.set_xticklabels(months, rotation=45, ha='right')
+      ax.legend()
+
+      # Adding bar labels
+      ax.bar_label(ax.containers[0], padding=3, color='black', fontsize=12, rotation=90)
+      ax.bar_label(ax.containers[1], padding=3, color='black', fontsize=12, rotation=90)
+
+      ax.spines[['top', 'left', 'bottom']].set_visible(False)
+      ax.spines['right'].set_linewidth(1.1)
+
+    # Plotting
+    fig, axs = plt.subplots(3, 1, figsize=(20, 25), dpi=96)
+    fig.suptitle(title, fontsize=16, weight='bold', alpha=.8)
+
+    plot_double_bar_chart(axs[0], 'heating', consumptions['Heating'][1], consumptions['Heating'][2],
+                          consumptions['Heating'][3])
+    plot_double_bar_chart(axs[1], 'cooling', consumptions['Cooling'][1], consumptions['Cooling'][2],
+                          consumptions['Cooling'][3])
+    plot_double_bar_chart(axs[2], 'dhw', consumptions['DHW'][1], consumptions['DHW'][2], consumptions['DHW'][3])
+
+    # Set a white background
+    fig.patch.set_facecolor('white')
+
+    # Adjust the margins around the plot area
+    plt.subplots_adjust(left=0.05, right=0.95, top=0.9, bottom=0.1, wspace=0.3, hspace=0.5)
+
+    # Save the plot
+    chart_path = self.charts_path / f'{file_name}.png'
+    plt.savefig(chart_path, bbox_inches='tight')
+    plt.close()
+
+    return chart_path
+
+  def yearly_consumption_comparison(self):
+    current_total_consumption = round(self.current_status_data['total_consumption'], 2)
+    retrofitted_total_consumption = round(self.retrofitted_data['total_consumption'], 2)
+    text = (
+      f'The total yearly energy consumption before and after the retrofit are {current_total_consumption} MWh and '
+      f'{retrofitted_total_consumption} MWh, respectively.')
+    if retrofitted_total_consumption < current_total_consumption:
+      change = str(round((current_total_consumption - retrofitted_total_consumption) * 100 / current_total_consumption,
+                         2))
+      text += f'Therefore, the total yearly energy consumption decreased by {change} \%.'
+    else:
+      change = str(round((retrofitted_total_consumption - current_total_consumption) * 100 /
+                         retrofitted_total_consumption, 2))
+      text += f'Therefore, the total yearly energy consumption increased by {change} \%. \par'
+    self.report.add_text(text)
+
+  def pv_system(self):
+    self.report.add_text('The first step in PV assessments is evaluating the potential of buildings for installing '
+                         'rooftop PV system. The benchmark value used for this evaluation is the mean yearly solar '
+                         'incident in Montreal. According to Hydro-Quebec, the mean annual incident in Montreal is 1350'
+                         'kWh/m2. Therefore, any building with rooftop annual global horizontal radiation of less than '
+                         '1080 kWh/m2 is considered to be infeasible. Table 4 shows the yearly horizontal radiation on '
+                         'buildings roofs. \par')
+    radiation_data = [
+      ["Building Name", "Roof Area $m^2$", "Function", "Rooftop Annual Global Horizontal Radiation kWh/ $m^2$"]
+    ]
+    pv_feasible_buildings = []
+    for building in self.city.buildings:
+      if building.roofs[0].global_irradiance[cte.YEAR][0] > 1080:
+        pv_feasible_buildings.append(building.name)
+      data = [building.name, str(format(building.roofs[0].perimeter_area, '.2f')), building.function,
+              str(format(building.roofs[0].global_irradiance[cte.YEAR][0] / (cte.WATTS_HOUR_TO_JULES * 1000), '.2f'))]
+      radiation_data.append(data)
+
+    self.report.add_table(radiation_data,
+                          caption='Buildings Roof Characteristics')
+
+    if len(pv_feasible_buildings) == len(self.city.buildings):
+      buildings_str = 'all'
+    else:
+      buildings_str = ", ".join(pv_feasible_buildings)
+    self.report.add_text(f"From the table it can be seen that {buildings_str} buildings are good candidates to have "
+                         f"rooftop PV system. The next step is calculating the amount of solar radiation on a tilted "
+                         f"surface. Figure 5 shows the total monthly solar radiation on a surface with the tilt angle "
+                         f"of 45 degrees on the roofs of those buildings that are identified to have rooftop PV system."
+                         f"\par")
+    tilted_radiation = self.plot_monthly_radiation()
+    self.report.add_image(str(tilted_radiation).replace('\\', '/'),
+                          caption='Total Monthly Solar Radiation on Buildings Roofs on a 45 Degrees Tilted Surface',
+                          placement='H')
+    self.report.add_text('The first step in sizing the PV system is to find the available roof area. '
+                         'Few considerations need to be made here. The considerations include space for maintenance '
+                         'crew, space for mechanical equipment, and orientation correction factor to make sure all '
+                         'the panel are truly facing south. After all these considerations, the minimum distance '
+                         'between the panels to avoid shading throughout the year is found. Table 5 shows the number of'
+                         'panles on each buildings roof, yearly PV production, total electricity consumption, and self '
+                         'consumption. \par')
+
+    pv_output_table = [['Building Name', 'Total Surface Area of PV Panels ($m^2$)',
+                        'Total Solar Incident on PV Modules (MWh)', 'Yearly PV production (MWh)']]
+
+    for building in self.city.buildings:
+      if building.name in pv_feasible_buildings:
+        pv_data = []
+        pv_data.append(building.name)
+        yearly_solar_incident = (building.roofs[0].global_irradiance_tilted[cte.YEAR][0] *
+                                 building.roofs[0].installed_solar_collector_area) / (cte.WATTS_HOUR_TO_JULES * 1e6)
+        yearly_solar_incident_str = format(yearly_solar_incident, '.2f')
+        yearly_pv_output = building.onsite_electrical_production[cte.YEAR][0] / (cte.WATTS_HOUR_TO_JULES * 1e6)
+        yearly_pv_output_str = format(yearly_pv_output, '.2f')
+
+        pv_data.append(format(building.roofs[0].installed_solar_collector_area, '.2f'))
+        pv_data.append(yearly_solar_incident_str)
+        pv_data.append(yearly_pv_output_str)
+
+        pv_output_table.append(pv_data)
+
+    self.report.add_table(pv_output_table, caption='PV System Simulation Results', first_column_width=3)
+
+  def life_cycle_cost_stacked_bar(self, file_name, title):
+    # Aggregate LCC components for current and retrofitted statuses
+    current_status_capex = 0
+    current_status_opex = 0
+    current_status_maintenance = 0
+    current_status_end_of_life = 0
+    retrofitted_capex = 0
+    retrofitted_opex = 0
+    retrofitted_maintenance = 0
+    retrofitted_end_of_life = 0
+    current_status_operational_income = 0
+    retrofitted_operational_income = 0
+
+    for building in self.city.buildings:
+      current_status_capex += self.current_status_lcc[f'{building.name}']['capital_cost_per_sqm']
+      retrofitted_capex += self.retrofitted_lcc[f'{building.name}']['capital_cost_per_sqm']
+      current_status_opex += self.current_status_lcc[f'{building.name}']['operational_cost_per_sqm']
+      retrofitted_opex += self.retrofitted_lcc[f'{building.name}']['operational_cost_per_sqm']
+      current_status_maintenance += self.current_status_lcc[f'{building.name}']['maintenance_cost_per_sqm']
+      retrofitted_maintenance += self.retrofitted_lcc[f'{building.name}']['maintenance_cost_per_sqm']
+      current_status_end_of_life += self.current_status_lcc[f'{building.name}']['end_of_life_cost_per_sqm']
+      retrofitted_end_of_life += self.retrofitted_lcc[f'{building.name}']['end_of_life_cost_per_sqm']
+      current_status_operational_income += self.current_status_lcc[f'{building.name}']['operational_income_per_sqm']
+      retrofitted_operational_income += self.retrofitted_lcc[f'{building.name}']['operational_income_per_sqm']
+
+    current_status_lcc_components_sqm = {
+      'Capital Cost': current_status_capex / len(self.city.buildings),
+      'Operational Cost': (current_status_opex - current_status_operational_income) / len(self.city.buildings),
+      'Maintenance Cost': current_status_maintenance / len(self.city.buildings),
+      'End of Life Cost': current_status_end_of_life / len(self.city.buildings),
+    }
+    retrofitted_lcc_components_sqm = {
+      'Capital Cost': retrofitted_capex / len(self.city.buildings),
+      'Operational Cost': (retrofitted_opex - retrofitted_operational_income) / len(self.city.buildings),
+      'Maintenance Cost': retrofitted_maintenance / len(self.city.buildings),
+      'End of Life Cost': retrofitted_end_of_life / len(self.city.buildings),
+    }
+
+    labels = ['Current Status', 'Retrofitted Status']
+    categories = ['Capital Cost', 'Operational Cost', 'Maintenance Cost', 'End of Life Cost']
+    colors = ['#2196f3', '#ff5a5f', '#4caf50', '#ffc107']  # Added new color
+
+    # Data preparation
+    bar_width = 0.35
+    r = np.arange(len(labels))
+
+    fig, ax = plt.subplots(figsize=(12, 8), dpi=96)
+    fig.suptitle(title, fontsize=16, weight='bold', alpha=.8)
+
+    # Plotting current status data
+    bottom = np.zeros(len(labels))
+    for category, color in zip(categories, colors):
+      values = [current_status_lcc_components_sqm[category], retrofitted_lcc_components_sqm[category]]
+      ax.bar(r, values, bottom=bottom, color=color, edgecolor='white', width=bar_width, label=category)
+      bottom += values
+
+    # Adding summation annotations at the top of the bars
+    for idx, (x, total) in enumerate(zip(r, bottom)):
+      ax.text(x, total, f'{total:.1f}', ha='center', va='bottom', fontsize=12, fontweight='bold')
+
+    # Adding labels, title, and grid
+    ax.set_xlabel('LCC Components', fontsize=12, labelpad=10)
+    ax.set_ylabel('Average Cost (CAD/m²)', fontsize=14, labelpad=10)
+    ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)
+    ax.set_xticks(r)
+    ax.set_xticklabels(labels, rotation=45, ha='right')
+    ax.legend()
+
+    # Adding a white background
+    fig.patch.set_facecolor('white')
+
+    # Adjusting the margins around the plot area
+    plt.subplots_adjust(left=0.05, right=0.95, top=0.9, bottom=0.2)
+
+    # Save the plot
+    chart_path = self.charts_path / f'{file_name}.png'
+    plt.savefig(chart_path, bbox_inches='tight')
+    plt.close()
+
+    return chart_path
+
+  def create_report(self):
+    # Add sections and text to the report
+    self.report.add_section('Overview of the Current Status in Buildings')
+    self.report.add_text('In this section, an overview of the current status of buildings characteristics, '
+                         'energy demand and consumptions are provided')
+    self.report.add_subsection('Buildings Characteristics')
+
+    self.building_energy_info()
+
+    # current monthly demands and consumptions
+    current_monthly_demands = self.current_status_data['monthly_demands']
+    current_monthly_consumptions = self.current_status_data['monthly_consumptions']
+
+    # Plot and save demand chart
+    current_demand_chart_path = self.plot_monthly_energy_demands(data=current_monthly_demands,
+                                                                 file_name='current_monthly_demands',
+                                                                 title='Current Status Monthly Energy Demands')
+    # Plot and save consumption chart
+    current_consumption_chart_path = self.plot_monthly_energy_consumption(data=current_monthly_consumptions,
+                                                                          file_name='monthly_consumptions',
+                                                                          title='Monthly Energy Consumptions')
+    current_consumption_breakdown_path = self.fuel_consumption_breakdown('City_Energy_Consumption_Breakdown',
+                                                                         self.current_status_data)
+    retrofitted_consumption_breakdown_path = self.fuel_consumption_breakdown(
+      'fuel_consumption_breakdown_after_retrofit',
+      self.retrofitted_data)
+    life_cycle_cost_sqm_stacked_bar_chart_path = self.life_cycle_cost_stacked_bar('lcc_per_sqm',
+                                                                                  'LCC Analysis')
+    # Add current state of energy demands in the city
+    self.report.add_subsection('Current State of Energy Demands in the City')
+    self.report.add_text('The total monthly energy demands in the city are shown in Figure 1. It should be noted '
+                         'that the electricity demand refers to total lighting and appliance electricity demands')
+    self.report.add_image(str(current_demand_chart_path).replace('\\', '/'),
+                          'Total Monthly Energy Demands in City',
+                          placement='h')
+
+    # Add current state of energy consumption in the city
+    self.report.add_subsection('Current State of Energy Consumption in the City')
+    self.report.add_text('The following figure shows the amount of energy consumed to supply heating, cooling, and '
+                         'domestic hot water needs in the city. The details of the systems in each building before '
+                         'and after retrofit are provided in Section 4. \par')
+    self.report.add_image(str(current_consumption_chart_path).replace('\\', '/'),
+                          'Total Monthly Energy Consumptions in City',
+                          placement='H')
+    self.report.add_text('Figure 3 shows the yearly energy supplied to the city by fuel in different sectors. '
+                         'All the values are in kWh.')
+    self.report.add_image(str(current_consumption_breakdown_path).replace('\\', '/'),
+                          'Current Energy Consumption Breakdown in the City by Fuel',
+                          placement='H')
+    self.report.add_section(f'{self.retrofit_scenario}')
+    self.report.add_subsection('Proposed Systems')
+    # self.energy_system_archetype_schematic()
+    if 'PV' in self.retrofit_scenario:
+      self.report.add_subsection('Rooftop Photovoltaic System Implementation')
+      self.pv_system()
+    if 'System' in self.retrofit_scenario:
+      self.report.add_subsection('Retrofitted HVAC and DHW Systems')
+      self.report.add_text('Figure 6 shows a comparison between total monthly energy consumption in the selected '
+                           'buildings before and after retrofitting.')
+      consumption_comparison = self.energy_consumption_comparison(self.current_status_data['monthly_consumptions'],
+                                                                  self.retrofitted_data['monthly_consumptions'],
+                                                                  'energy_consumption_comparison_in_city',
+                                                                  'Total Monthly Energy Consumption Comparison in '
+                                                                  'Buildings')
+      self.report.add_image(str(consumption_comparison).replace('\\', '/'),
+                            caption='Comparison of Total Monthly Energy Consumption in City Buildings',
+                            placement='H')
+      self.yearly_consumption_comparison()
+      self.report.add_text('Figure 7 shows the fuel consumption breakdown in the area after the retrofit.')
+      self.report.add_image(str(retrofitted_consumption_breakdown_path).replace('\\', '/'),
+                            caption=f'Fuel Consumption Breakdown After {self.retrofit_scenario}',
+                            placement='H')
+    self.report.add_subsection('Life Cycle Cost Analysis')
+    self.report.add_image(str(life_cycle_cost_sqm_stacked_bar_chart_path).replace('\\', '/'),
+                          caption='Average Life Cycle Cost Components',
+                          placement='H')
+
+    # Save and compile the report
+    self.report.save_report()
+    self.report.compile_to_pdf()

energy_system_modelling_package/energy_system_retrofit/energy_system_retrofit_results.py

@@ -0,0 +1,176 @@
+import hub.helpers.constants as cte
+
+
+def hourly_electricity_consumption_profile(building):
+  hourly_electricity_consumption = []
+  energy_systems = building.energy_systems
+  appliance = building.appliances_electrical_demand[cte.HOUR]
+  lighting = building.lighting_electrical_demand[cte.HOUR]
+  elec_heating = 0
+  elec_cooling = 0
+  elec_dhw = 0
+  if cte.HEATING in building.energy_consumption_breakdown[cte.ELECTRICITY]:
+    elec_heating = 1
+  if cte.COOLING in building.energy_consumption_breakdown[cte.ELECTRICITY]:
+    elec_cooling = 1
+  if cte.DOMESTIC_HOT_WATER in building.energy_consumption_breakdown[cte.ELECTRICITY]:
+    elec_dhw = 1
+  heating = None
+  cooling = None
+  dhw = None
+  if elec_heating == 1:
+    for energy_system in energy_systems:
+      if cte.HEATING in energy_system.demand_types:
+        for generation_system in energy_system.generation_systems:
+          if generation_system.fuel_type == cte.ELECTRICITY:
+            if cte.HEATING in generation_system.energy_consumption:
+              heating = generation_system.energy_consumption[cte.HEATING][cte.HOUR]
+            else:
+              if len(energy_system.generation_systems) > 1:
+                heating = [x / 2 for x in building.heating_consumption[cte.HOUR]]
+              else:
+                heating = building.heating_consumption[cte.HOUR]
+  if elec_dhw == 1:
+    for energy_system in energy_systems:
+      if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
+        for generation_system in energy_system.generation_systems:
+          if generation_system.fuel_type == cte.ELECTRICITY:
+            if cte.DOMESTIC_HOT_WATER in generation_system.energy_consumption:
+              dhw = generation_system.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR]
+            else:
+              if len(energy_system.generation_systems) > 1:
+                dhw = [x / 2 for x in building.domestic_hot_water_consumption[cte.HOUR]]
+              else:
+                dhw = building.domestic_hot_water_consumption[cte.HOUR]
+
+  if elec_cooling == 1:
+    for energy_system in energy_systems:
+      if cte.COOLING in energy_system.demand_types:
+        for generation_system in energy_system.generation_systems:
+          if cte.COOLING in generation_system.energy_consumption:
+            cooling = generation_system.energy_consumption[cte.COOLING][cte.HOUR]
+          else:
+            if len(energy_system.generation_systems) > 1:
+              cooling = [x / 2 for x in building.cooling_consumption[cte.HOUR]]
+            else:
+              cooling = building.cooling_consumption[cte.HOUR]
+
+  for i in range(len(building.heating_demand[cte.HOUR])):
+    hourly = 0
+    hourly += appliance[i] / 3600
+    hourly += lighting[i] / 3600
+    if heating is not None:
+      hourly += heating[i] / 3600
+    if cooling is not None:
+      hourly += cooling[i] / 3600
+    if dhw is not None:
+      hourly += dhw[i] / 3600
+    hourly_electricity_consumption.append(hourly)
+  return hourly_electricity_consumption
+
+
+def consumption_data(city):
+  energy_consumption_data = {}
+  for building in city.buildings:
+    hourly_electricity_consumption = hourly_electricity_consumption_profile(building)
+    energy_consumption_data[f'{building.name}'] = {'heating_consumption': building.heating_consumption,
+                                                   'cooling_consumption': building.cooling_consumption,
+                                                   'domestic_hot_water_consumption':
+                                                   building.domestic_hot_water_consumption,
+                                                   'energy_consumption_breakdown':
+                                                   building.energy_consumption_breakdown,
+                                                   'hourly_electricity_consumption': hourly_electricity_consumption}
+  peak_electricity_consumption = 0
+  for building in energy_consumption_data:
+    peak_electricity_consumption += max(energy_consumption_data[building]['hourly_electricity_consumption'])
+  heating_demand_monthly = []
+  cooling_demand_monthly = []
+  dhw_demand_monthly = []
+  lighting_appliance_monthly = []
+  heating_consumption_monthly = []
+  cooling_consumption_monthly = []
+  dhw_consumption_monthly = []
+  for i in range(12):
+    heating_demand = 0
+    cooling_demand = 0
+    dhw_demand = 0
+    lighting_appliance_demand = 0
+    heating_consumption = 0
+    cooling_consumption = 0
+    dhw_consumption = 0
+    for building in city.buildings:
+      heating_demand += building.heating_demand[cte.MONTH][i] / 3.6e6
+      cooling_demand += building.cooling_demand[cte.MONTH][i] / 3.6e6
+      dhw_demand += building.domestic_hot_water_heat_demand[cte.MONTH][i] / 3.6e6
+      lighting_appliance_demand += building.lighting_electrical_demand[cte.MONTH][i] / 3.6e6
+      heating_consumption += building.heating_consumption[cte.MONTH][i] / 3.6e6
+      if building.cooling_consumption[cte.YEAR][0] == 0:
+        cooling_consumption += building.cooling_demand[cte.MONTH][i] / (3.6e6 * 2)
+      else:
+        cooling_consumption += building.cooling_consumption[cte.MONTH][i] / 3.6e6
+      dhw_consumption += building.domestic_hot_water_consumption[cte.MONTH][i] / 3.6e6
+    heating_demand_monthly.append(heating_demand)
+    cooling_demand_monthly.append(cooling_demand)
+    dhw_demand_monthly.append(dhw_demand)
+    lighting_appliance_monthly.append(lighting_appliance_demand)
+    heating_consumption_monthly.append(heating_consumption)
+    cooling_consumption_monthly.append(cooling_consumption)
+    dhw_consumption_monthly.append(dhw_consumption)
+
+  monthly_demands = {'heating': heating_demand_monthly,
+                     'cooling': cooling_demand_monthly,
+                     'dhw': dhw_demand_monthly,
+                     'lighting_appliance': lighting_appliance_monthly}
+  monthly_consumptions = {'heating': heating_consumption_monthly,
+                          'cooling': cooling_consumption_monthly,
+                          'dhw': dhw_consumption_monthly}
+  yearly_heating = 0
+  yearly_cooling = 0
+  yearly_dhw = 0
+  yearly_appliance = 0
+  yearly_lighting = 0
+  for building in city.buildings:
+    yearly_appliance += building.appliances_electrical_demand[cte.YEAR][0] / 3.6e9
+    yearly_lighting += building.lighting_electrical_demand[cte.YEAR][0] / 3.6e9
+    yearly_heating += building.heating_consumption[cte.YEAR][0] / 3.6e9
+    yearly_cooling += building.cooling_consumption[cte.YEAR][0] / 3.6e9
+    yearly_dhw += building.domestic_hot_water_consumption[cte.YEAR][0] / 3.6e9
+
+  total_consumption = yearly_heating + yearly_cooling + yearly_dhw + yearly_appliance + yearly_lighting
+  energy_consumption_data['monthly_demands'] = monthly_demands
+  energy_consumption_data['monthly_consumptions'] = monthly_consumptions
+  energy_consumption_data['total_consumption'] = total_consumption
+  energy_consumption_data['maximum_hourly_electricity_consumption'] = peak_electricity_consumption
+
+  return energy_consumption_data
+
+
+def cost_data(building, lcc_dataframe, cost_retrofit_scenario):
+  total_floor_area = 0
+  for thermal_zone in building.thermal_zones_from_internal_zones:
+    total_floor_area += thermal_zone.total_floor_area
+  capital_cost = lcc_dataframe.loc['total_capital_costs_systems', f'Scenario {cost_retrofit_scenario}']
+  operational_cost = lcc_dataframe.loc['total_operational_costs', f'Scenario {cost_retrofit_scenario}']
+  maintenance_cost = lcc_dataframe.loc['total_maintenance_costs', f'Scenario {cost_retrofit_scenario}']
+  end_of_life_cost = lcc_dataframe.loc['end_of_life_costs', f'Scenario {cost_retrofit_scenario}']
+  operational_income = lcc_dataframe.loc['operational_incomes', f'Scenario {cost_retrofit_scenario}']
+  total_life_cycle_cost = capital_cost + operational_cost + maintenance_cost + end_of_life_cost + operational_income
+  specific_capital_cost = capital_cost / total_floor_area
+  specific_operational_cost = operational_cost / total_floor_area
+  specific_maintenance_cost = maintenance_cost / total_floor_area
+  specific_end_of_life_cost = end_of_life_cost / total_floor_area
+  specific_operational_income = operational_income / total_floor_area
+  specific_life_cycle_cost = total_life_cycle_cost / total_floor_area
+  life_cycle_cost_analysis = {'capital_cost': capital_cost,
+                              'capital_cost_per_sqm': specific_capital_cost,
+                              'operational_cost': operational_cost,
+                              'operational_cost_per_sqm': specific_operational_cost,
+                              'maintenance_cost': maintenance_cost,
+                              'maintenance_cost_per_sqm': specific_maintenance_cost,
+                              'end_of_life_cost': end_of_life_cost,
+                              'end_of_life_cost_per_sqm': specific_end_of_life_cost,
+                              'operational_income': operational_income,
+                              'operational_income_per_sqm': specific_operational_income,
+                              'total_life_cycle_cost': total_life_cycle_cost,
+                              'total_life_cycle_cost_per_sqm': specific_life_cycle_cost}
+  return life_cycle_cost_analysis

energy_system_modelling_package/random_assignation.py

@@ -15,8 +15,8 @@ from hub.city_model_structure.building import Building
 energy_systems_format = 'montreal_custom'
 
 # parameters:
-residential_systems_percentage = {'system 1 gas': 100,
-                                  'system 1 electricity': 0,
+residential_systems_percentage = {'system 1 gas': 15,
+                                  'system 1 electricity': 35,
                                   'system 2 gas': 0,
                                   'system 2 electricity': 0,
                                   'system 3 and 4 gas': 0,
@@ -25,16 +25,46 @@ residential_systems_percentage = {'system 1 gas': 100,
                                   'system 5 electricity': 0,
                                   'system 6 gas': 0,
                                   'system 6 electricity': 0,
-                                  'system 8 gas': 0,
-                                  'system 8 electricity': 0}
+                                  'system 8 gas': 15,
+                                  'system 8 electricity': 35}
 
-residential_new_systems_percentage = {'PV+ASHP+GasBoiler+TES': 0,
-                                      'PV+4Pipe+DHW': 100,
-                                      'PV+ASHP+ElectricBoiler+TES': 0,
-                                      'PV+GSHP+GasBoiler+TES': 0,
-                                      'PV+GSHP+ElectricBoiler+TES': 0,
-                                      'PV+WSHP+GasBoiler+TES': 0,
-                                      'PV+WSHP+ElectricBoiler+TES': 0}
+residential_new_systems_percentage = {
+  'Central Hydronic Air and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 100,
+  'Central Hydronic Air and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
+  'Central Hydronic Ground and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
+  'Central Hydronic Ground and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW '
+  'and Grid Tied PV': 0,
+  'Central Hydronic Water and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
+  'Central Hydronic Water and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW '
+  'and Grid Tied PV': 0,
+  'Central Hydronic Air and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
+  'Central Hydronic Air and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
+  'Central Hydronic Ground and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
+  'Central Hydronic Ground and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
+  'Central Hydronic Water and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
+  'Central Hydronic Water and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
+  'Grid Tied PV System': 0,
+  'system 1 gas': 0,
+  'system 1 gas grid tied pv': 0,
+  'system 1 electricity': 0,
+  'system 1 electricity grid tied pv': 0,
+  'system 2 gas': 0,
+  'system 2 gas grid tied pv': 0,
+  'system 2 electricity': 0,
+  'system 2 electricity grid tied pv': 0,
+  'system 3 and 4 gas': 0,
+  'system 3 and 4 gas grid tied pv': 0,
+  'system 3 and 4 electricity': 0,
+  'system 3 and 4 electricity grid tied pv': 0,
+  'system 6 gas': 0,
+  'system 6 gas grid tied pv': 0,
+  'system 6 electricity': 0,
+  'system 6 electricity grid tied pv': 0,
+  'system 8 gas': 0,
+  'system 8 gas grid tied pv': 0,
+  'system 8 electricity': 0,
+  'system 8 electricity grid tied pv': 0,
+}
 
 non_residential_systems_percentage = {'system 1 gas': 0,
                                       'system 1 electricity': 0,
@@ -110,4 +140,3 @@ def call_random(_buildings: [Building], _systems_percentage):
       _buildings[_selected_buildings[_position]].energy_systems_archetype_name = case['system']
       _position += 1
   return _buildings
-

energy_system_modelling_package/report_creation.py

@@ -0,0 +1,119 @@
+import subprocess
+import datetime
+import os
+from pathlib import Path
+
+
+class LatexReport:
+  def __init__(self, file_name, title, subtitle, output_path):
+    self.file_name = file_name
+    self.output_path = Path(output_path) / 'report'
+    self.output_path.mkdir(parents=True, exist_ok=True)
+    self.file_path = self.output_path / f"{file_name}.tex"
+    self.content = []
+    self.content.append(r'\documentclass{article}')
+    self.content.append(r'\usepackage[margin=2.5cm]{geometry}')
+    self.content.append(r'\usepackage{graphicx}')
+    self.content.append(r'\usepackage{tabularx}')
+    self.content.append(r'\usepackage{multirow}')
+    self.content.append(r'\usepackage{float}')
+    self.content.append(r'\begin{document}')
+
+    current_datetime = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+    self.content.append(r'\title{' + title + '}')
+    self.content.append(r'\author{Next-Generation Cities Institute}')
+    self.content.append(r'\date{}')
+    self.content.append(r'\maketitle')
+
+    self.content.append(r'\begin{center}')
+    self.content.append(r'\large ' + subtitle + r'\\')
+    self.content.append(r'\large ' + current_datetime)
+    self.content.append(r'\end{center}')
+
+  def add_section(self, section_title):
+    self.content.append(r'\section{' + section_title + r'}')
+
+  def add_subsection(self, subsection_title):
+    self.content.append(r'\subsection{' + subsection_title + r'}')
+
+  def add_subsubsection(self, subsection_title):
+    self.content.append(r'\subsubsection{' + subsection_title + r'}')
+
+  def add_text(self, text):
+    self.content.append(text)
+
+  def add_table(self, table_data, caption=None, first_column_width=None, merge_first_column=False):
+    num_columns = len(table_data[0])
+    total_width = 0.9
+    first_column_width_str = ''
+
+    if first_column_width is not None:
+      first_column_width_str = str(first_column_width) + 'cm'
+      total_width -= first_column_width / 16.0
+
+    if caption:
+      self.content.append(r'\begin{table}[htbp]')
+      self.content.append(r'\caption{' + caption + r'}')
+      self.content.append(r'\centering')
+
+    column_format = '|p{' + first_column_width_str + r'}|' + '|'.join(
+      ['X'] * (num_columns - 1)) + '|' if first_column_width is not None else '|' + '|'.join(['X'] * num_columns) + '|'
+    self.content.append(r'\begin{tabularx}{\textwidth}{' + column_format + '}')
+    self.content.append(r'\hline')
+
+    previous_first_column = None
+    rowspan_count = 1
+
+    for i, row in enumerate(table_data):
+      if merge_first_column and i > 0 and row[0] == previous_first_column:
+        rowspan_count += 1
+        self.content.append(' & '.join(['' if j == 0 else cell for j, cell in enumerate(row)]) + r' \\')
+      else:
+        if merge_first_column and i > 0 and rowspan_count > 1:
+          self.content[-rowspan_count] = self.content[-rowspan_count].replace(r'\multirow{1}',
+                                                                              r'\multirow{' + str(rowspan_count) + '}')
+          rowspan_count = 1
+        if merge_first_column and i < len(table_data) - 1 and row[0] == table_data[i + 1][0]:
+          self.content.append(r'\multirow{1}{*}{' + row[0] + '}' + ' & ' + ' & '.join(row[1:]) + r' \\')
+        else:
+          self.content.append(' & '.join(row) + r' \\')
+        previous_first_column = row[0]
+      self.content.append(r'\hline')
+
+    if merge_first_column and rowspan_count > 1:
+      self.content[-rowspan_count] = self.content[-rowspan_count].replace(r'\multirow{1}',
+                                                                          r'\multirow{' + str(rowspan_count) + '}')
+
+    self.content.append(r'\end{tabularx}')
+
+    if caption:
+      self.content.append(r'\end{table}')
+
+  def add_image(self, image_path, caption=None, placement='ht'):
+    if caption:
+      self.content.append(r'\begin{figure}[' + placement + r']')
+      self.content.append(r'\centering')
+      self.content.append(r'\includegraphics[width=\textwidth]{' + image_path + r'}')
+      self.content.append(r'\caption{' + caption + r'}')
+      self.content.append(r'\end{figure}')
+    else:
+      self.content.append(r'\begin{figure}[' + placement + r']')
+      self.content.append(r'\centering')
+      self.content.append(r'\includegraphics[width=\textwidth]{' + image_path + r'}')
+      self.content.append(r'\end{figure}')
+
+  def add_itemize(self, items):
+    self.content.append(r'\begin{itemize}')
+    for item in items:
+      self.content.append(r'\item ' + item)
+    self.content.append(r'\end{itemize}')
+
+  def save_report(self):
+    self.content.append(r'\end{document}')
+    with open(self.file_path, 'w') as f:
+      f.write('\n'.join(self.content))
+
+  def compile_to_pdf(self):
+    subprocess.run(['pdflatex', '-output-directory', str(self.output_path), str(self.file_path)])
+

energy_system_retrofit.py

@@ -1,64 +1,114 @@
-from scripts.geojson_creator import process_geojson
 from pathlib import Path
 import subprocess
-from scripts.ep_run_enrich import energy_plus_workflow
+from building_modelling.ep_run_enrich import energy_plus_workflow
+from energy_system_modelling_package.energy_system_modelling_factories.montreal_energy_system_archetype_modelling_factory import \
+  MontrealEnergySystemArchetypesSimulationFactory
+from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.pv_system_assessment import \
+  PvSystemAssessment
+from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.solar_calculator import \
+  SolarCalculator
 from hub.imports.geometry_factory import GeometryFactory
 from hub.helpers.dictionaries import Dictionaries
 from hub.imports.construction_factory import ConstructionFactory
 from hub.imports.usage_factory import UsageFactory
 from hub.imports.weather_factory import WeatherFactory
 from hub.imports.results_factory import ResultFactory
-from scripts.energy_system_analysis_report import EnergySystemAnalysisReport
-from scripts import random_assignation
+from energy_system_modelling_package.energy_system_retrofit.energy_system_retrofit_report import EnergySystemRetrofitReport
+from building_modelling.geojson_creator import process_geojson
+from energy_system_modelling_package import random_assignation
 from hub.imports.energy_systems_factory import EnergySystemsFactory
-from scripts.energy_system_sizing import SystemSizing
-from scripts.energy_system_retrofit_results import system_results, new_system_results
-from scripts.energy_system_sizing_and_simulation_factory import EnergySystemsSimulationFactory
-from scripts.costs.cost import Cost
-from scripts.costs.constants import SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT_AND_PV
-import hub.helpers.constants as cte
+from energy_system_modelling_package.energy_system_modelling_factories.energy_system_sizing_factory import EnergySystemsSizingFactory
+from energy_system_modelling_package.energy_system_retrofit.energy_system_retrofit_results import consumption_data, cost_data
+from costing_package.cost import Cost
+from costing_package.constants import SYSTEM_RETROFIT_AND_PV, CURRENT_STATUS
 from hub.exports.exports_factory import ExportsFactory
+
 # Specify the GeoJSON file path
-geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.0001)
-file_path = (Path(__file__).parent / 'input_files' / 'output_buildings.geojson')
-# Specify the output path for the PDF file
+main_path = Path(__file__).parent.resolve()
+input_files_path = (Path(__file__).parent / 'input_files')
+input_files_path.mkdir(parents=True, exist_ok=True)
+geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.00006, path=main_path)
+geojson_file_path = input_files_path / 'output_buildings.geojson'
 output_path = (Path(__file__).parent / 'out_files').resolve()
-# Create city object from GeoJSON file
-city = GeometryFactory('geojson',
-                       path=file_path,
+output_path.mkdir(parents=True, exist_ok=True)
+energy_plus_output_path = output_path / 'energy_plus_outputs'
+energy_plus_output_path.mkdir(parents=True, exist_ok=True)
+simulation_results_path = (Path(__file__).parent / 'out_files' / 'simulation_results').resolve()
+simulation_results_path.mkdir(parents=True, exist_ok=True)
+sra_output_path = output_path / 'sra_outputs'
+sra_output_path.mkdir(parents=True, exist_ok=True)
+pv_assessment_path = output_path / 'pv_outputs'
+pv_assessment_path.mkdir(parents=True, exist_ok=True)
+cost_analysis_output_path = output_path / 'cost_analysis'
+cost_analysis_output_path.mkdir(parents=True, exist_ok=True)
+city = GeometryFactory(file_type='geojson',
+                       path=geojson_file_path,
                        height_field='height',
                        year_of_construction_field='year_of_construction',
                        function_field='function',
                        function_to_hub=Dictionaries().montreal_function_to_hub_function).city
-# Enrich city data
 ConstructionFactory('nrcan', city).enrich()
-
 UsageFactory('nrcan', city).enrich()
 WeatherFactory('epw', city).enrich()
-ExportsFactory('sra', city, output_path).export()
-sra_path = (output_path / f'{city.name}_sra.xml').resolve()
+ExportsFactory('sra', city, sra_output_path).export()
+sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
 subprocess.run(['sra', str(sra_path)])
-ResultFactory('sra', city, output_path).enrich()
-energy_plus_workflow(city)
+ResultFactory('sra', city, sra_output_path).enrich()
+energy_plus_workflow(city, energy_plus_output_path)
 random_assignation.call_random(city.buildings, random_assignation.residential_systems_percentage)
 EnergySystemsFactory('montreal_custom', city).enrich()
-SystemSizing(city.buildings).montreal_custom()
-current_system = new_system_results(city.buildings)
+EnergySystemsSizingFactory('peak_load_sizing', city).enrich()
+current_status_energy_consumption = consumption_data(city)
+current_status_life_cycle_cost = {}
+for building in city.buildings:
+  cost_retrofit_scenario = CURRENT_STATUS
+  lcc_dataframe = Cost(building=building,
+                       retrofit_scenario=cost_retrofit_scenario,
+                       fuel_tariffs=['Electricity-D', 'Gas-Energir']).life_cycle
+  lcc_dataframe.to_csv(cost_analysis_output_path / f'{building.name}_current_status_lcc.csv')
+  current_status_life_cycle_cost[f'{building.name}'] = cost_data(building, lcc_dataframe, cost_retrofit_scenario)
 random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
 EnergySystemsFactory('montreal_future', city).enrich()
+EnergySystemsSizingFactory('peak_load_sizing', city).enrich()
+# # Initialize solar calculation parameters (e.g., azimuth, altitude) and compute irradiance and solar angles
+tilt_angle = 37
+solar_parameters = SolarCalculator(city=city,
+                                   surface_azimuth_angle=180,
+                                   tilt_angle=tilt_angle,
+                                   standard_meridian=-75)
+solar_angles = solar_parameters.solar_angles  # Obtain solar angles for further analysis
+solar_parameters.tilted_irradiance_calculator()  # Calculate the solar radiation on a tilted surface
 for building in city.buildings:
-  EnergySystemsSimulationFactory('archetype1', building=building, output_path=output_path).enrich()
-  print(building.energy_consumption_breakdown[cte.ELECTRICITY][cte.COOLING] +
-        building.energy_consumption_breakdown[cte.ELECTRICITY][cte.HEATING] +
-        building.energy_consumption_breakdown[cte.ELECTRICITY][cte.DOMESTIC_HOT_WATER])
-new_system = new_system_results(city.buildings)
-# EnergySystemAnalysisReport(city, output_path).create_report(current_system, new_system)
+  MontrealEnergySystemArchetypesSimulationFactory(f'archetype_cluster_{building.energy_systems_archetype_cluster_id}',
+                                                  building,
+                                                  simulation_results_path).enrich()
+  if 'PV' in building.energy_systems_archetype_name:
+    PvSystemAssessment(building=building,
+                       pv_system=None,
+                       battery=None,
+                       electricity_demand=None,
+                       tilt_angle=tilt_angle,
+                       solar_angles=solar_angles,
+                       pv_installation_type='rooftop',
+                       simulation_model_type='explicit',
+                       module_model_name=None,
+                       inverter_efficiency=0.95,
+                       system_catalogue_handler=None,
+                       roof_percentage_coverage=0.75,
+                       facade_coverage_percentage=0,
+                       csv_output=False,
+                       output_path=pv_assessment_path).enrich()
+retrofitted_energy_consumption = consumption_data(city)
+retrofitted_life_cycle_cost = {}
 for building in city.buildings:
-  costs = Cost(building=building, retrofit_scenario=SYSTEM_RETROFIT_AND_PV).life_cycle
-  costs.to_csv(output_path / f'{building.name}_lcc.csv')
-  (costs.loc['global_operational_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].
-   to_csv(output_path / f'{building.name}_op.csv'))
-  costs.loc['global_capital_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].to_csv(
-    output_path / f'{building.name}_cc.csv')
-  costs.loc['global_maintenance_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].to_csv(
-    output_path / f'{building.name}_m.csv')
+  cost_retrofit_scenario = SYSTEM_RETROFIT_AND_PV
+  lcc_dataframe = Cost(building=building,
+                       retrofit_scenario=cost_retrofit_scenario,
+                       fuel_tariffs=['Electricity-D', 'Gas-Energir']).life_cycle
+  lcc_dataframe.to_csv(cost_analysis_output_path / f'{building.name}_retrofitted_lcc.csv')
+  retrofitted_life_cycle_cost[f'{building.name}'] = cost_data(building, lcc_dataframe, cost_retrofit_scenario)
+EnergySystemRetrofitReport(city, output_path, 'PV Implementation and System Retrofit',
+                           current_status_energy_consumption, retrofitted_energy_consumption,
+                           current_status_life_cycle_cost, retrofitted_life_cycle_cost).create_report()
+
+

example_codes/pv_system_assessment.py

@@ -0,0 +1,86 @@
+from pathlib import Path
+import subprocess
+from building_modelling.ep_run_enrich import energy_plus_workflow
+from energy_system_modelling_package import random_assignation
+from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.pv_system_assessment import \
+  PvSystemAssessment
+from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.solar_calculator import \
+  SolarCalculator
+from hub.imports.energy_systems_factory import EnergySystemsFactory
+from hub.imports.geometry_factory import GeometryFactory
+from hub.helpers.dictionaries import Dictionaries
+from hub.imports.construction_factory import ConstructionFactory
+from hub.imports.usage_factory import UsageFactory
+from hub.imports.weather_factory import WeatherFactory
+from hub.imports.results_factory import ResultFactory
+from building_modelling.geojson_creator import process_geojson
+from hub.exports.exports_factory import ExportsFactory
+import hub.helpers.constants as cte
+# Define paths for input and output directories, ensuring directories are created if they do not exist
+main_path = Path(__file__).parent.parent.resolve()
+input_files_path = (Path(__file__).parent.parent / 'input_files')
+input_files_path.mkdir(parents=True, exist_ok=True)
+output_path = (Path(__file__).parent.parent / 'out_files').resolve()
+output_path.mkdir(parents=True, exist_ok=True)
+# Define specific paths for outputs from EnergyPlus and SRA (Simplified Radiosity Algorith) and PV calculation processes
+energy_plus_output_path = output_path / 'energy_plus_outputs'
+energy_plus_output_path.mkdir(parents=True, exist_ok=True)
+sra_output_path = output_path / 'sra_outputs'
+sra_output_path.mkdir(parents=True, exist_ok=True)
+pv_assessment_path = output_path / 'pv_outputs'
+pv_assessment_path.mkdir(parents=True, exist_ok=True)
+# Generate a GeoJSON file for city buildings based on latitude, longitude, and building dimensions
+geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, path=main_path, diff=0.0001)
+geojson_file_path = input_files_path / 'output_buildings.geojson'
+# Initialize a city object from the geojson file, mapping building functions using a predefined dictionary
+city = GeometryFactory(file_type='geojson',
+                       path=geojson_file_path,
+                       height_field='height',
+                       year_of_construction_field='year_of_construction',
+                       function_field='function',
+                       function_to_hub=Dictionaries().montreal_function_to_hub_function).city
+# Enrich city data with construction, usage, and weather information specific to the location
+ConstructionFactory('nrcan', city).enrich()
+UsageFactory('nrcan', city).enrich()
+WeatherFactory('epw', city).enrich()
+# Execute the EnergyPlus workflow to simulate building energy performance and generate output
+# energy_plus_workflow(city, energy_plus_output_path)
+# Export the city data in SRA-compatible format to facilitate solar radiation assessment
+ExportsFactory('sra', city, sra_output_path).export()
+# Run SRA simulation using an external command, passing the generated SRA XML file path as input
+sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
+subprocess.run(['sra', str(sra_path)])
+# Enrich city data with SRA simulation results for subsequent analysis
+ResultFactory('sra', city, sra_output_path).enrich()
+# Assign PV system archetype name to the buildings in city
+random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
+# Enrich city model with Montreal future systems parameters
+EnergySystemsFactory('montreal_future', city).enrich()
+# # Initialize solar calculation parameters (e.g., azimuth, altitude) and compute irradiance and solar angles
+tilt_angle = 37
+solar_parameters = SolarCalculator(city=city,
+                                   surface_azimuth_angle=180,
+                                   tilt_angle=tilt_angle,
+                                   standard_meridian=-75)
+solar_angles = solar_parameters.solar_angles  # Obtain solar angles for further analysis
+solar_parameters.tilted_irradiance_calculator()  # Calculate the solar radiation on a tilted surface
+# # PV modelling building by building
+#List of available PV modules ['RE400CAA Pure 2', 'RE410CAA Pure 2', 'RE420CAA Pure 2', 'RE430CAA Pure 2',
+# 'REC600AA Pro M', 'REC610AA Pro M', 'REC620AA Pro M', 'REC630AA Pro M', 'REC640AA Pro M']
+for building in city.buildings:
+  PvSystemAssessment(building=building,
+                     pv_system=None,
+                     battery=None,
+                     tilt_angle=tilt_angle,
+                     solar_angles=solar_angles,
+                     pv_installation_type='rooftop',
+                     simulation_model_type='explicit',
+                     module_model_name='REC640AA Pro M',
+                     inverter_efficiency=0.95,
+                     system_catalogue_handler='montreal_future',
+                     roof_percentage_coverage=0.75,
+                     facade_coverage_percentage=0,
+                     csv_output=False,
+                     output_path=pv_assessment_path).enrich()
+
+

hub/catalog_factories/construction/eilat_catalog.py

@@ -22,6 +22,7 @@ class EilatCatalog(Catalog):
   """
   Eilat catalog class
   """
+
   def __init__(self, path):
     _path_archetypes = Path(path / 'eilat_archetypes.json').resolve()
     _path_constructions = (path / 'eilat_constructions.json').resolve()
@@ -121,8 +122,14 @@ class EilatCatalog(Catalog):
       construction_period = archetype['period_of_construction']
       average_storey_height = archetype['average_storey_height']
       extra_loses_due_to_thermal_bridges = archetype['extra_loses_due_thermal_bridges']
-      infiltration_rate_for_ventilation_system_off = archetype['infiltration_rate_for_ventilation_system_off'] / cte.HOUR_TO_SECONDS
-      infiltration_rate_for_ventilation_system_on = archetype['infiltration_rate_for_ventilation_system_on'] / cte.HOUR_TO_SECONDS
+      infiltration_rate_for_ventilation_system_off = archetype[
+                                                       'infiltration_rate_for_ventilation_system_off'] / cte.HOUR_TO_SECONDS
+      infiltration_rate_for_ventilation_system_on = archetype[
+                                                      'infiltration_rate_for_ventilation_system_on'] / cte.HOUR_TO_SECONDS
+      infiltration_rate_area_for_ventilation_system_off = archetype[
+                                                       'infiltration_rate_area_for_ventilation_system_off']
+      infiltration_rate_area_for_ventilation_system_on = archetype[
+                                                      'infiltration_rate_area_for_ventilation_system_on']
 
       archetype_constructions = []
       for archetype_construction in archetype['constructions']:
@@ -160,7 +167,9 @@ class EilatCatalog(Catalog):
                                            extra_loses_due_to_thermal_bridges,
                                            None,
                                            infiltration_rate_for_ventilation_system_off,
-                                           infiltration_rate_for_ventilation_system_on))
+                                           infiltration_rate_for_ventilation_system_on,
+                                           infiltration_rate_area_for_ventilation_system_off,
+                                           infiltration_rate_area_for_ventilation_system_on))
     return _catalog_archetypes
 
   def names(self, category=None):

hub/catalog_factories/construction/nrcan_catalog.py

@@ -128,6 +128,12 @@ class NrcanCatalog(Catalog):
       infiltration_rate_for_ventilation_system_on = (
         archetype['infiltration_rate_for_ventilation_system_on'] / cte.HOUR_TO_SECONDS
       )
+      infiltration_rate_area_for_ventilation_system_off = (
+              archetype['infiltration_rate_area_for_ventilation_system_off'] * 1
+      )
+      infiltration_rate_area_for_ventilation_system_on = (
+              archetype['infiltration_rate_area_for_ventilation_system_on'] * 1
+      )
 
       archetype_constructions = []
       for archetype_construction in archetype['constructions']:
@@ -153,7 +159,6 @@ class NrcanCatalog(Catalog):
                                          _window)
             archetype_constructions.append(_construction)
             break
-
       _catalog_archetypes.append(Archetype(archetype_id,
                                            name,
                                            function,
@@ -165,7 +170,10 @@ class NrcanCatalog(Catalog):
                                            extra_loses_due_to_thermal_bridges,
                                            None,
                                            infiltration_rate_for_ventilation_system_off,
-                                           infiltration_rate_for_ventilation_system_on))
+                                           infiltration_rate_for_ventilation_system_on,
+                                           infiltration_rate_area_for_ventilation_system_off,
+                                           infiltration_rate_area_for_ventilation_system_on
+                                           ))
     return _catalog_archetypes
 
   def names(self, category=None):

hub/catalog_factories/construction/nrel_catalog.py

@@ -129,6 +129,12 @@ class NrelCatalog(Catalog):
       infiltration_rate_for_ventilation_system_on = float(
         archetype['infiltration_rate_for_ventilation_system_on']['#text']
       ) / cte.HOUR_TO_SECONDS
+      infiltration_rate_area_for_ventilation_system_off = float(
+        archetype['infiltration_rate_area_for_ventilation_system_on']['#text']
+      )
+      infiltration_rate_area_for_ventilation_system_on = float(
+        archetype['infiltration_rate_area_for_ventilation_system_on']['#text']
+      )
 
       archetype_constructions = []
       for archetype_construction in archetype['constructions']['construction']:
@@ -162,7 +168,9 @@ class NrelCatalog(Catalog):
                                            extra_loses_due_to_thermal_bridges,
                                            indirect_heated_ratio,
                                            infiltration_rate_for_ventilation_system_off,
-                                           infiltration_rate_for_ventilation_system_on))
+                                           infiltration_rate_for_ventilation_system_on,
+                                           infiltration_rate_area_for_ventilation_system_off,
+                                           infiltration_rate_area_for_ventilation_system_on))
     return _catalog_archetypes
 
   def names(self, category=None):

hub/catalog_factories/cost/montreal_complete_cost_catalog.py

@@ -48,7 +48,7 @@ class MontrealNewCatalog(Catalog):
       construction = float(archetype['incomes']['subsidies']['construction']['#text'])
       hvac = float(archetype['incomes']['subsidies']['hvac']['#text'])
       photovoltaic_system = float(archetype['incomes']['subsidies']['photovoltaic']['#text'])
-      electricity_exports = float(archetype['incomes']['electricity_export']['#text']) / 1000 / 3600
+      electricity_exports = float(archetype['incomes']['electricity_export']['#text'])
       reduction_tax = float(archetype['incomes']['tax_reduction']['#text']) / 100
       income = Income(construction_subsidy=construction,
                       hvac_subsidy=hvac,

hub/catalog_factories/data_models/construction/archetype.py

@@ -23,7 +23,10 @@ class Archetype:
                extra_loses_due_to_thermal_bridges,
                indirect_heated_ratio,
                infiltration_rate_for_ventilation_system_off,
-               infiltration_rate_for_ventilation_system_on):
+               infiltration_rate_for_ventilation_system_on,
+               infiltration_rate_area_for_ventilation_system_off,
+               infiltration_rate_area_for_ventilation_system_on
+               ):
     self._id = archetype_id
     self._name = name
     self._function = function
@@ -36,6 +39,8 @@ class Archetype:
     self._indirect_heated_ratio = indirect_heated_ratio
     self._infiltration_rate_for_ventilation_system_off = infiltration_rate_for_ventilation_system_off
     self._infiltration_rate_for_ventilation_system_on = infiltration_rate_for_ventilation_system_on
+    self._infiltration_rate_area_for_ventilation_system_off = infiltration_rate_area_for_ventilation_system_off
+    self._infiltration_rate_area_for_ventilation_system_on = infiltration_rate_area_for_ventilation_system_on
 
   @property
   def id(self):
@@ -133,6 +138,22 @@ class Archetype:
     """
     return self._infiltration_rate_for_ventilation_system_on
 
+  @property
+  def infiltration_rate_area_for_ventilation_system_off(self):
+    """
+    Get archetype infiltration rate for ventilation system off in m3/sm2
+    :return: float
+    """
+    return self._infiltration_rate_area_for_ventilation_system_off
+
+  @property
+  def infiltration_rate_area_for_ventilation_system_on(self):
+    """
+    Get archetype infiltration rate for ventilation system on in m3/sm2
+    :return: float
+    """
+    return self._infiltration_rate_for_ventilation_system_on
+
   def to_dictionary(self):
     """Class content to dictionary"""
     _constructions = []
@@ -149,6 +170,8 @@ class Archetype:
                              'indirect heated ratio': self.indirect_heated_ratio,
                              'infiltration rate for ventilation off [1/s]': self.infiltration_rate_for_ventilation_system_off,
                              'infiltration rate for ventilation on [1/s]': self.infiltration_rate_for_ventilation_system_on,
+                             'infiltration rate area for ventilation off [m3/sm2]': self.infiltration_rate_area_for_ventilation_system_off,
+                             'infiltration rate area for ventilation on [m3/sm2]': self.infiltration_rate_area_for_ventilation_system_on,
                              'constructions': _constructions
                              }
                }

hub/catalog_factories/data_models/energy_systems/archetype.py

@@ -15,11 +15,20 @@ class Archetype:
   """
   Archetype class
   """
-  def __init__(self, name, systems):
 
+  def __init__(self, name, systems, archetype_cluster_id=None):
+    self._cluster_id = archetype_cluster_id
     self._name = name
     self._systems = systems
 
+  @property
+  def cluster_id(self):
+    """
+    Get id
+    :return: string
+    """
+    return self._cluster_id
+
   @property
   def name(self):
     """
@@ -43,8 +52,9 @@ class Archetype:
       _systems.append(_system.to_dictionary())
     content = {
       'Archetype': {
+        'cluster_id': self.cluster_id,
         'name': self.name,
         'systems': _systems
-        }
       }
+    }
     return content

hub/catalog_factories/data_models/energy_systems/emission_system.py

@@ -10,7 +10,7 @@ class EmissionSystem:
   """
   Emission system class
   """
-  def __init__(self, system_id, model_name=None, system_type=None, parasitic_energy_consumption=None):
+  def __init__(self, system_id, model_name=None, system_type=None, parasitic_energy_consumption=0):
 
     self._system_id = system_id
     self._model_name = model_name

hub/catalog_factories/data_models/energy_systems/pv_generation_system.py

@@ -17,8 +17,9 @@ class PvGenerationSystem(GenerationSystem):
   def __init__(self, system_id, name, system_type, model_name=None, manufacturer=None, electricity_efficiency=None,
                nominal_electricity_output=None, nominal_ambient_temperature=None, nominal_cell_temperature=None,
                nominal_radiation=None, standard_test_condition_cell_temperature=None,
-               standard_test_condition_maximum_power=None, cell_temperature_coefficient=None, width=None, height=None,
-               distribution_systems=None, energy_storage_systems=None):
+               standard_test_condition_maximum_power=None, standard_test_condition_radiation=None,
+               cell_temperature_coefficient=None, width=None, height=None, distribution_systems=None,
+               energy_storage_systems=None):
     super().__init__(system_id=system_id, name=name, model_name=model_name,
                      manufacturer=manufacturer, fuel_type='renewable', distribution_systems=distribution_systems,
                      energy_storage_systems=energy_storage_systems)
@@ -30,6 +31,7 @@ class PvGenerationSystem(GenerationSystem):
     self._nominal_radiation = nominal_radiation
     self._standard_test_condition_cell_temperature = standard_test_condition_cell_temperature
     self._standard_test_condition_maximum_power = standard_test_condition_maximum_power
+    self._standard_test_condition_radiation = standard_test_condition_radiation
     self._cell_temperature_coefficient = cell_temperature_coefficient
     self._width = width
     self._height = height
@@ -98,6 +100,15 @@ class PvGenerationSystem(GenerationSystem):
     """
     return self._standard_test_condition_maximum_power
 
+  @property
+  def standard_test_condition_radiation(self):
+    """
+    Get standard test condition cell temperature of PV panels in W/m2
+    :return: float
+    """
+    return self._standard_test_condition_radiation
+
+
   @property
   def cell_temperature_coefficient(self):
     """
@@ -143,6 +154,7 @@ class PvGenerationSystem(GenerationSystem):
         'nominal radiation [W/m2]': self.nominal_radiation,
         'standard test condition cell temperature [Celsius]': self.standard_test_condition_cell_temperature,
         'standard test condition maximum power [W]': self.standard_test_condition_maximum_power,
+        'standard test condition radiation [W/m2]': self.standard_test_condition_radiation,
         'cell temperature coefficient': self.cell_temperature_coefficient,
         'width': self.width,
         'height': self.height,

hub/catalog_factories/data_models/energy_systems/thermal_storage_system.py

@@ -119,7 +119,7 @@ class ThermalStorageSystem(EnergyStorageSystem):
         'height [m]': self.height,
         'layers': _layers,
         'maximum operating temperature [Celsius]': self.maximum_operating_temperature,
-        'storage_medium': self.storage_medium.to_dictionary(),
+        'storage_medium': _medias,
         'heating coil capacity [W]': self.heating_coil_capacity
       }
     }

hub/catalog_factories/energy_systems/montreal_custom_catalog.py

@@ -135,7 +135,7 @@ class MontrealCustomCatalog(Catalog):
       equipment_id = float(equipment['@id'])
       equipment_type = equipment['@type']
       model_name = equipment['name']
-      parasitic_consumption = None
+      parasitic_consumption = 0
       if 'parasitic_consumption' in equipment:
         parasitic_consumption = float(equipment['parasitic_consumption']['#text']) / 100
 

hub/catalog_factories/energy_systems/montreal_future_system_catalogue.py

@@ -30,7 +30,8 @@ class MontrealFutureSystemCatalogue(Catalog):
     path = str(path / 'montreal_future_systems.xml')
     with open(path, 'r', encoding='utf-8') as xml:
       self._archetypes = xmltodict.parse(xml.read(),
-                                         force_list=['pv_generation_component', 'templateStorages', 'demand'])
+                                         force_list=['pv_generation_component', 'templateStorages', 'demand',
+                                                     'system', 'system_id'])
 
     self._storage_components = self._load_storage_components()
     self._generation_components = self._load_generation_components()
@@ -49,7 +50,7 @@ class MontrealFutureSystemCatalogue(Catalog):
       'non_pv_generation_component']
     if non_pv_generation_components is not None:
       for non_pv in non_pv_generation_components:
-        system_id = non_pv['system_id']
+        system_id = non_pv['generation_system_id']
         name = non_pv['name']
         system_type = non_pv['system_type']
         model_name = non_pv['model_name']
@@ -181,7 +182,7 @@ class MontrealFutureSystemCatalogue(Catalog):
       'pv_generation_component']
     if pv_generation_components is not None:
       for pv in pv_generation_components:
-        system_id = pv['system_id']
+        system_id = pv['generation_system_id']
         name = pv['name']
         system_type = pv['system_type']
         model_name = pv['model_name']
@@ -193,6 +194,7 @@ class MontrealFutureSystemCatalogue(Catalog):
         nominal_radiation = pv['nominal_radiation']
         standard_test_condition_cell_temperature = pv['standard_test_condition_cell_temperature']
         standard_test_condition_maximum_power = pv['standard_test_condition_maximum_power']
+        standard_test_condition_radiation = pv['standard_test_condition_radiation']
         cell_temperature_coefficient = pv['cell_temperature_coefficient']
         width = pv['width']
         height = pv['height']
@@ -215,6 +217,7 @@ class MontrealFutureSystemCatalogue(Catalog):
                                           standard_test_condition_cell_temperature=
                                           standard_test_condition_cell_temperature,
                                           standard_test_condition_maximum_power=standard_test_condition_maximum_power,
+                                          standard_test_condition_radiation=standard_test_condition_radiation,
                                           cell_temperature_coefficient=cell_temperature_coefficient,
                                           width=width,
                                           height=height,
@@ -262,7 +265,7 @@ class MontrealFutureSystemCatalogue(Catalog):
         system_id = None
         model_name = None
         system_type = None
-        parasitic_energy_consumption = None
+        parasitic_energy_consumption = 0
         emission_system = EmissionSystem(system_id=system_id,
                                          model_name=model_name,
                                          system_type=system_type,
@@ -379,6 +382,7 @@ class MontrealFutureSystemCatalogue(Catalog):
     _system_archetypes = []
     system_clusters = self._archetypes['EnergySystemCatalog']['system_archetypes']['system_archetype']
     for system_cluster in system_clusters:
+      archetype_id = system_cluster['@cluster_id']
       name = system_cluster['name']
       systems = system_cluster['systems']['system_id']
       integer_system_ids = [int(item) for item in systems]
@@ -386,7 +390,7 @@ class MontrealFutureSystemCatalogue(Catalog):
       for system_archetype in self._systems:
         if int(system_archetype.id) in integer_system_ids:
           _systems.append(system_archetype)
-      _system_archetypes.append(Archetype(name=name, systems=_systems))
+      _system_archetypes.append(Archetype(archetype_cluster_id=archetype_id, name=name, systems=_systems))
     return _system_archetypes
 
   def _load_materials(self):

hub/city_model_structure/building.py

@@ -92,7 +92,7 @@ class Building(CityObject):
         logging.error('Building %s [%s] has an unexpected surface type %s.', self.name, self.aliases, surface.type)
     self._domestic_hot_water_peak_load = None
     self._fuel_consumption_breakdown = {}
-    self._pv_generation = {}
+    self._systems_archetype_cluster_id = None
 
   @property
   def shell(self) -> Polyhedron:
@@ -810,39 +810,16 @@ class Building(CityObject):
     Get total electricity produced onsite in J
     return: dict
     """
-    orientation_losses_factor = {cte.MONTH: {'north': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-                                             'east': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-                                             'south': [2.137931, 1.645503, 1.320946, 1.107817, 0.993213, 0.945175,
-                                                       0.967949, 1.065534, 1.24183, 1.486486, 1.918033, 2.210526],
-                                             'west': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]},
-                                 cte.YEAR: {'north': [0],
-                                            'east': [0],
-                                            'south': [1.212544],
-                                            'west': [0]}
-                                 }
-
-    # Add other systems whenever new ones appear
-    if self.energy_systems is None:
-      return self._onsite_electrical_production
-    for energy_system in self.energy_systems:
-      for generation_system in energy_system.generation_systems:
-        if generation_system.system_type == cte.PHOTOVOLTAIC:
-          if generation_system.electricity_efficiency is not None:
-            _efficiency = float(generation_system.electricity_efficiency)
-          else:
-            _efficiency = 0
-          self._onsite_electrical_production = {}
-          for _key in self.roofs[0].global_irradiance.keys():
-            _results = [0 for _ in range(0, len(self.roofs[0].global_irradiance[_key]))]
-            for surface in self.roofs:
-              if _key in orientation_losses_factor:
-                _results = [x + y * _efficiency * surface.perimeter_area
-                            * surface.solar_collectors_area_reduction_factor * z
-                            for x, y, z in zip(_results, surface.global_irradiance[_key],
-                                               orientation_losses_factor[_key]['south'])]
-            self._onsite_electrical_production[_key] = _results
     return self._onsite_electrical_production
 
+  @onsite_electrical_production.setter
+  def onsite_electrical_production(self, value):
+    """
+    set onsite electrical production from external pv simulations
+    :return:
+    """
+    self._onsite_electrical_production = value
+
   @property
   def lower_corner(self):
     """
@@ -863,60 +840,71 @@ class Building(CityObject):
     Get energy consumption of different sectors
     return: dict
     """
-    fuel_breakdown = {cte.ELECTRICITY: {cte.LIGHTING: self.lighting_electrical_demand[cte.YEAR][0],
-                                        cte.APPLIANCES: self.appliances_electrical_demand[cte.YEAR][0]}}
+    fuel_breakdown = {cte.ELECTRICITY: {cte.LIGHTING: self.lighting_electrical_demand[cte.YEAR][0] if self.lighting_electrical_demand else 0,
+                                        cte.APPLIANCES: self.appliances_electrical_demand[cte.YEAR][0] if self.appliances_electrical_demand else 0}}
     energy_systems = self.energy_systems
-    for energy_system in energy_systems:
-      demand_types = energy_system.demand_types
-      generation_systems = energy_system.generation_systems
-      for demand_type in demand_types:
-        for generation_system in generation_systems:
-          if generation_system.system_type != cte.PHOTOVOLTAIC:
-            if generation_system.fuel_type not in fuel_breakdown:
-              fuel_breakdown[generation_system.fuel_type] = {}
-            if demand_type in generation_system.energy_consumption:
-              fuel_breakdown[f'{generation_system.fuel_type}'][f'{demand_type}'] = (
-                generation_system.energy_consumption)[f'{demand_type}'][cte.YEAR][0]
-            storage_systems = generation_system.energy_storage_systems
-            if storage_systems:
-              for storage_system in storage_systems:
-                if storage_system.type_energy_stored == 'thermal' and storage_system.heating_coil_energy_consumption:
-                  fuel_breakdown[cte.ELECTRICITY][f'{demand_type}'] += storage_system.heating_coil_energy_consumption[cte.YEAR][0]
-    #TODO: When simulation models of all energy system archetypes are created, this part can be removed
-    heating_fuels = []
-    dhw_fuels = []
-    for energy_system in self.energy_systems:
-      if cte.HEATING in energy_system.demand_types:
-        for generation_system in energy_system.generation_systems:
-          heating_fuels.append(generation_system.fuel_type)
-      if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
-        for generation_system in energy_system.generation_systems:
-          dhw_fuels.append(generation_system.fuel_type)
-    for key in fuel_breakdown:
-      if key == cte.ELECTRICITY and cte.COOLING not in fuel_breakdown[key]:
-        for energy_system in energy_systems:
-          if cte.COOLING in energy_system.demand_types and cte.COOLING not in fuel_breakdown[key]:
-            for generation_system in energy_system.generation_systems:
-              fuel_breakdown[generation_system.fuel_type][cte.COOLING] = self.cooling_consumption[cte.YEAR][0]
-      for fuel in heating_fuels:
-        if cte.HEATING not in fuel_breakdown[fuel]:
+    if energy_systems is not None:
+      for energy_system in energy_systems:
+        demand_types = energy_system.demand_types
+        generation_systems = energy_system.generation_systems
+        for demand_type in demand_types:
+          for generation_system in generation_systems:
+            if generation_system.system_type != cte.PHOTOVOLTAIC:
+              if generation_system.fuel_type not in fuel_breakdown:
+                fuel_breakdown[generation_system.fuel_type] = {}
+              if demand_type in generation_system.energy_consumption:
+                fuel_breakdown[f'{generation_system.fuel_type}'][f'{demand_type}'] = (
+                  generation_system.energy_consumption)[f'{demand_type}'][cte.YEAR][0]
+              storage_systems = generation_system.energy_storage_systems
+              if storage_systems:
+                for storage_system in storage_systems:
+                  if storage_system.type_energy_stored == 'thermal' and storage_system.heating_coil_energy_consumption:
+                    fuel_breakdown[cte.ELECTRICITY][f'{demand_type}'] += (
+                      storage_system.heating_coil_energy_consumption)[f'{demand_type}'][cte.YEAR][0]
+      #TODO: When simulation models of all energy system archetypes are created, this part can be removed
+      heating_fuels = []
+      dhw_fuels = []
+      for energy_system in self.energy_systems:
+        if cte.HEATING in energy_system.demand_types:
+          for generation_system in energy_system.generation_systems:
+            heating_fuels.append(generation_system.fuel_type)
+        if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
+          for generation_system in energy_system.generation_systems:
+            dhw_fuels.append(generation_system.fuel_type)
+      for key in fuel_breakdown:
+        if key == cte.ELECTRICITY and cte.COOLING not in fuel_breakdown[key]:
           for energy_system in energy_systems:
-            if cte.HEATING in energy_system.demand_types:
-              for generation_system in energy_system.generation_systems:
-                fuel_breakdown[generation_system.fuel_type][cte.HEATING] = self.heating_consumption[cte.YEAR][0]
-      for fuel in dhw_fuels:
-        if cte.DOMESTIC_HOT_WATER not in fuel_breakdown[fuel]:
-          for energy_system in energy_systems:
-            if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
-              for generation_system in energy_system.generation_systems:
-                fuel_breakdown[generation_system.fuel_type][cte.DOMESTIC_HOT_WATER] = self.domestic_hot_water_consumption[cte.YEAR][0]
+            if cte.COOLING in energy_system.demand_types and cte.COOLING not in fuel_breakdown[key]:
+              if self.cooling_consumption:
+                fuel_breakdown[energy_system.generation_systems[0].fuel_type][cte.COOLING] = self.cooling_consumption[cte.YEAR][0]
+        for fuel in heating_fuels:
+          if cte.HEATING not in fuel_breakdown[fuel]:
+            for energy_system in energy_systems:
+              if cte.HEATING in energy_system.demand_types:
+                if self.heating_consumption:
+                  fuel_breakdown[energy_system.generation_systems[0].fuel_type][cte.HEATING] = self.heating_consumption[cte.YEAR][0]
+        for fuel in dhw_fuels:
+          if cte.DOMESTIC_HOT_WATER not in fuel_breakdown[fuel]:
+            for energy_system in energy_systems:
+              if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
+                if self.domestic_hot_water_consumption:
+                  fuel_breakdown[energy_system.generation_systems[0].fuel_type][cte.DOMESTIC_HOT_WATER] = self.domestic_hot_water_consumption[cte.YEAR][0]
     self._fuel_consumption_breakdown = fuel_breakdown
     return self._fuel_consumption_breakdown
 
   @property
-  def pv_generation(self) -> dict:
-    return self._pv_generation
+  def energy_systems_archetype_cluster_id(self):
+    """
+    Get energy systems archetype id
+    :return: str
+    """
+    return self._systems_archetype_cluster_id
+
+  @energy_systems_archetype_cluster_id.setter
+  def energy_systems_archetype_cluster_id(self, value):
+    """
+    Set energy systems archetype id
+    :param value: str
+    """
+    self._systems_archetype_cluster_id = value
 
-  @pv_generation.setter
-  def pv_generation(self, value):
-    self._pv_generation = value

hub/city_model_structure/building_demand/internal_zone.py

@@ -132,6 +132,8 @@ class InternalZone:
       _thermal_boundary = ThermalBoundary(surface, surface.solid_polygon.area, windows_areas)
       surface.associated_thermal_boundaries = [_thermal_boundary]
       _thermal_boundaries.append(_thermal_boundary)
+    if self.thermal_archetype is None:
+      return None  # there are no archetype
     _number_of_storeys = int(self.volume / self.area / self.thermal_archetype.average_storey_height)
     _thermal_zone = ThermalZone(_thermal_boundaries, self, self.volume, self.area, _number_of_storeys)
     for thermal_boundary in _thermal_zone.thermal_boundaries:

hub/city_model_structure/building_demand/surface.py

@@ -42,7 +42,7 @@ class Surface:
     self._short_wave_reflectance = None
     self._long_wave_emittance = None
     self._inverse = None
-    self._associated_thermal_boundaries = []
+    self._associated_thermal_boundaries = None
     self._vegetation = None
     self._percentage_shared = None
     self._solar_collectors_area_reduction_factor = None
@@ -157,6 +157,7 @@ class Surface:
     if self._inclination is None:
       self._inclination = np.arccos(self.perimeter_polygon.normal[2])
     return self._inclination
+
   @property
   def type(self):
     """
@@ -180,7 +181,7 @@ class Surface:
   @property
   def global_irradiance(self) -> dict:
     """
-    Get global irradiance on surface in J/m2
+    Get global irradiance on surface in W/m2
     :return: dict
     """
     return self._global_irradiance
@@ -188,7 +189,7 @@ class Surface:
   @global_irradiance.setter
   def global_irradiance(self, value):
     """
-    Set global irradiance on surface in J/m2
+    Set global irradiance on surface in W/m2
     :param value: dict
     """
     self._global_irradiance = value
@@ -390,7 +391,7 @@ class Surface:
   @property
   def global_irradiance_tilted(self) -> dict:
     """
-    Get global irradiance on a tilted surface in J/m2
+    Get global irradiance on a tilted surface in W/m2
     :return: dict
     """
     return self._global_irradiance_tilted
@@ -398,7 +399,7 @@ class Surface:
   @global_irradiance_tilted.setter
   def global_irradiance_tilted(self, value):
     """
-    Set global irradiance on a tilted surface in J/m2
+    Set global irradiance on a tilted surface in W/m2
     :param value: dict
     """
     self._global_irradiance_tilted = value

hub/city_model_structure/building_demand/thermal_archetype.py

@@ -20,6 +20,8 @@ class ThermalArchetype:
     self._indirect_heated_ratio = None
     self._infiltration_rate_for_ventilation_system_off = None
     self._infiltration_rate_for_ventilation_system_on = None
+    self._infiltration_rate_area_for_ventilation_system_off = None
+    self._infiltration_rate_area_for_ventilation_system_on = None
 
   @property
   def constructions(self) -> [Construction]:
@@ -132,3 +134,35 @@ class ThermalArchetype:
     :param value: float
     """
     self._infiltration_rate_for_ventilation_system_on = value
+
+  @property
+  def infiltration_rate_area_for_ventilation_system_off(self):
+    """
+    Get infiltration rate for ventilation system off in l/s/m2
+    :return: float
+    """
+    return self._infiltration_rate_area_for_ventilation_system_off
+
+  @infiltration_rate_area_for_ventilation_system_off.setter
+  def infiltration_rate_area_for_ventilation_system_off(self, value):
+    """
+    Set infiltration rate for ventilation system off in l/s/m2
+    :param value: float
+    """
+    self._infiltration_rate_area_for_ventilation_system_off = value
+
+  @property
+  def infiltration_rate_area_for_ventilation_system_on(self):
+    """
+    Get infiltration rate for ventilation system on in l/s/m2
+    :return: float
+    """
+    return self._infiltration_rate_area_for_ventilation_system_on
+
+  @infiltration_rate_area_for_ventilation_system_on.setter
+  def infiltration_rate_area_for_ventilation_system_on(self, value):
+    """
+    Set infiltration rate for ventilation system on in l/s/m2
+    :param value: float
+    """
+    self._infiltration_rate_area_for_ventilation_system_on = value

hub/city_model_structure/building_demand/thermal_zone.py

@@ -44,6 +44,8 @@ class ThermalZone:
     self._indirectly_heated_area_ratio = None
     self._infiltration_rate_system_on = None
     self._infiltration_rate_system_off = None
+    self._infiltration_rate_area_system_on = None
+    self._infiltration_rate_area_system_off = None
     self._volume = volume
     self._ordinate_number = None
     self._view_factors_matrix = None
@@ -166,6 +168,24 @@ class ThermalZone:
     self._infiltration_rate_system_off = self._parent_internal_zone.thermal_archetype.infiltration_rate_for_ventilation_system_off
     return self._infiltration_rate_system_off
 
+  @property
+  def infiltration_rate_area_system_on(self):
+    """
+    Get thermal zone infiltration rate system on in air changes per second (1/s)
+    :return: None or float
+    """
+    self._infiltration_rate_area_system_on = self._parent_internal_zone.thermal_archetype.infiltration_rate_area_for_ventilation_system_on
+    return self._infiltration_rate_area_system_on
+
+  @property
+  def infiltration_rate_area_system_off(self):
+    """
+    Get thermal zone infiltration rate system off in air changes per second (1/s)
+    :return: None or float
+    """
+    self._infiltration_rate_area_system_off = self._parent_internal_zone.thermal_archetype.infiltration_rate_area_for_ventilation_system_off
+    return self._infiltration_rate_area_system_off
+
   @property
   def volume(self):
     """

hub/city_model_structure/energy_systems/emission_system.py

@@ -13,7 +13,7 @@ class EmissionSystem:
   def __init__(self):
     self._model_name = None
     self._type = None
-    self._parasitic_energy_consumption = None
+    self._parasitic_energy_consumption = 0
 
   @property
   def model_name(self):

hub/city_model_structure/energy_systems/thermal_storage_system.py

@@ -24,7 +24,7 @@ class ThermalStorageSystem(EnergyStorageSystem):
     self._maximum_operating_temperature = None
     self._heating_coil_capacity = None
     self._temperature = None
-    self._heating_coil_energy_consumption = None
+    self._heating_coil_energy_consumption = {}
 
   @property
   def volume(self):

hub/data/construction/eilat_archetypes.json

@@ -8,6 +8,8 @@
       "extra_loses_due_thermal_bridges": 0.1,
       "infiltration_rate_for_ventilation_system_on": 0,
       "infiltration_rate_for_ventilation_system_off": 0.9,
+      "infiltration_rate_area_for_ventilation_system_on": 0,
+      "infiltration_rate_area_for_ventilation_system_off": 0.006,
       "constructions": {
         "OutdoorsWall": {
           "opaque_surface_name": "residential_1000_1980_BWh",
@@ -42,6 +44,8 @@
       "extra_loses_due_thermal_bridges": 0.1,
       "infiltration_rate_for_ventilation_system_on": 0,
       "infiltration_rate_for_ventilation_system_off": 0.31,
+      "infiltration_rate_area_for_ventilation_system_on": 0,
+      "infiltration_rate_area_for_ventilation_system_off": 0.002,
       "constructions": {
         "OutdoorsWall": {
           "opaque_surface_name": "dormitory_2011_3000_BWh",
@@ -76,6 +80,8 @@
       "extra_loses_due_thermal_bridges": 0.09,
       "infiltration_rate_for_ventilation_system_on": 0,
       "infiltration_rate_for_ventilation_system_off": 0.65,
+      "infiltration_rate_area_for_ventilation_system_on": 0,
+      "infiltration_rate_area_for_ventilation_system_off": 0.004,
       "constructions": {
         "OutdoorsWall": {
           "opaque_surface_name": "hotel_employees_1981_2010_BWh",

hub/data/construction/us_archetypes.xml

@@ -21,6 +21,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.5</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.003</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="2" building_type="medium office" reference_standard="ASHRAE 90.1_2004" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -44,6 +46,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.50</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.003</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="3" building_type="large office" reference_standard="ASHRAE 90.1_2004" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -67,6 +71,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.50</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.003</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="4" building_type="primary school" reference_standard="ASHRAE 90.1_2004" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -89,6 +95,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.50</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.003</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="5" building_type="secondary school" reference_standard="ASHRAE 90.1_2004" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -111,6 +119,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.50</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.003</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="6" building_type="stand-alone retail" reference_standard="ASHRAE 90.1_2004" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -133,6 +143,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.50</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.003</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="7" building_type="strip mall" reference_standard="ASHRAE 90.1_2004" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -155,6 +167,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.50</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.003</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="8" building_type="supermarket" reference_standard="ASHRAE 90.1_2004" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -177,6 +191,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.50</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.003</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="9" building_type="quick service restaurant" reference_standard="ASHRAE 90.1_2004" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -199,6 +215,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.50</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.003</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="10" building_type="full service restaurant" reference_standard="ASHRAE 90.1_2004" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -221,6 +239,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.50</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.003</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="11" building_type="small hotel" reference_standard="ASHRAE 90.1_2004" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -243,6 +263,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.50</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.003</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="12" building_type="large hotel" reference_standard="ASHRAE 90.1_2004" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -265,6 +287,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.50</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.003</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="13" building_type="hospital" reference_standard="ASHRAE 90.1_2004" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -287,6 +311,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.50</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.003</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="14" building_type="outpatient healthcare" reference_standard="ASHRAE 90.1_2004" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -309,6 +335,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.50</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.003</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="15" building_type="warehouse" reference_standard="ASHRAE 90.1_2004" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -331,6 +359,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.50</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.003</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="16" building_type="midrise apartment" reference_standard="ASHRAE 90.1_2004" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -353,6 +383,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.50</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.003</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="17" building_type="high-rise apartment" reference_standard="ASHRAE 90.1_2004" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -375,6 +407,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.50</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.003</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="18" building_type="small office" reference_standard="ASHRAE 189.1_2009" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -397,6 +431,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.1</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.0005</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="19" building_type="medium office" reference_standard="ASHRAE 189.1_2009" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -419,6 +455,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.1</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.0005</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="20" building_type="large office" reference_standard="ASHRAE 189.1_2009" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -441,6 +479,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.1</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.0005</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="21" building_type="primary school" reference_standard="ASHRAE 189.1_2009" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -463,6 +503,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.1</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.0005</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="22" building_type="secondary school" reference_standard="ASHRAE 189.1_2009" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -485,6 +527,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.1</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.0005</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="23" building_type="stand-alone retail" reference_standard="ASHRAE 189.1_2009" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -507,6 +551,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.1</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.0005</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="24" building_type="strip mall" reference_standard="ASHRAE 189.1_2009" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -529,6 +575,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.1</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.0005</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="25" building_type="supermarket" reference_standard="ASHRAE 189.1_2009" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -551,6 +599,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.10</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.0005</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="26" building_type="quick service restaurant" reference_standard="ASHRAE 189.1_2009" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -573,6 +623,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.10</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.0005</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="27" building_type="full service restaurant" reference_standard="ASHRAE 189.1_2009" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -595,6 +647,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.10</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.0005</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="28" building_type="small hotel" reference_standard="ASHRAE 189.1_2009" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -617,6 +671,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.10</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.0005</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="29" building_type="large hotel" reference_standard="ASHRAE 189.1_2009" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -639,6 +695,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.10</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.0005</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="30" building_type="hospital" reference_standard="ASHRAE 189.1_2009" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -661,6 +719,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.10</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.0005</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="31" building_type="outpatient healthcare" reference_standard="ASHRAE 189.1_2009" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -683,6 +743,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.10</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.0005</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="32" building_type="warehouse" reference_standard="ASHRAE 189.1_2009" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -705,6 +767,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.10</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.0005</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="33" building_type="midrise apartment" reference_standard="ASHRAE 189.1_2009" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -727,6 +791,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.10</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.0005</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="34" building_type="high-rise apartment" reference_standard="ASHRAE 189.1_2009" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -749,6 +815,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.10</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.0005</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="35" building_type="residential" reference_standard="ASHRAE 189.1_2009" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -771,6 +839,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.10</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.0005</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="36" building_type="residential" reference_standard="ASHRAE 90.1_2004" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -793,6 +863,8 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.50</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.003</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
     <archetype id="37" building_type="industry" reference_standard="non_standard_dompark" climate_zone="ASHRAE_2004:4A">
         <constructions>
@@ -815,5 +887,7 @@
         <indirect_heated_ratio units="-">0.15</indirect_heated_ratio>
         <infiltration_rate_for_ventilation_system_off units="ACH">0.10</infiltration_rate_for_ventilation_system_off>
         <infiltration_rate_for_ventilation_system_on units="ACH">0</infiltration_rate_for_ventilation_system_on>
+        <infiltration_rate_area_for_ventilation_system_off units="ACH">0.0005</infiltration_rate_area_for_ventilation_system_off>
+        <infiltration_rate_area_for_ventilation_system_on units="ACH">0</infiltration_rate_area_for_ventilation_system_on>
     </archetype>
 </archetypes>

hub/data/costs/montreal_costs_completed.xml

@@ -187,7 +187,7 @@
 				<hvac cost_unit="%">1.5</hvac>
 				<photovoltaic cost_unit="%">3.6</photovoltaic>
 			</subsidies>
-            <electricity_export cost_unit="currency/kWh">0.07</electricity_export>
+            <electricity_export cost_unit="currency/kWh">0.075</electricity_export>
             <tax_reduction cost_unit="%">5</tax_reduction>
         </incomes>
     </archetype>

hub/data/energy_systems/montreal_custom_systems.xml

@@ -198,7 +198,7 @@
             <equipments>
                 <generation_id>3</generation_id>
                 <distribution_id>8</distribution_id>
-g            </equipments>
+            </equipments>
         </system>
         <system id="5">
             <name>Single zone packaged rooftop unit with electrical resistance furnace and baseboards and fuel boiler for acs</name>
@@ -240,7 +240,7 @@ g            </equipments>
             <demand>domestic_hot_water</demand>
         </demands>
         <equipments>
-            <generation_id>2</generation_id>
+            <generation_id>1</generation_id>
             <distribution_id>3</distribution_id>
         </equipments>
         </system>
@@ -302,7 +302,7 @@ g            </equipments>
             </demands>
             <equipments>
                 <generation_id>5</generation_id>
-                <distribution_id>6</distribution_id>
+                <distribution_id>4</distribution_id>
             </equipments>
         </system>
         <system id="15">

hub/exports/building_energy/idf.py

@@ -7,6 +7,9 @@ Code contributors: Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concord
                    Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
 """
 import copy
+import datetime
+import glob
+import os
 from pathlib import Path
 from geomeppy import IDF
 import hub.helpers.constants as cte
@@ -275,11 +278,12 @@ class Idf:
     _kwargs[f'Field_{counter + 2}'] = 'Until: 24:00,0.0'
     self._idf.newidfobject(self._COMPACT_SCHEDULE, **_kwargs)
 
-  def _write_schedules_file(self, usage, schedule):
-    file_name = str((Path(self._output_path) / f'{schedule.type} schedules {usage}.csv').resolve())
-    with open(file_name, 'w', encoding='utf8') as file:
-      for value in schedule.values:
-        file.write(f'{str(value)},\n')
+  def _write_schedules_file(self, schedule, usage):
+    file_name = str((Path(self._output_path) / f'{schedule.type} schedules {usage.replace("/","_")}.csv').resolve())
+    if not Path(file_name).exists():
+      with open(file_name, 'w', encoding='utf8') as file:
+        for value in schedule.values:
+          file.write(f'{str(value)},\n')
     return Path(file_name).name
 
   def _add_file_schedule(self, usage, schedule, file_name):
@@ -304,7 +308,7 @@ class Idf:
       for schedule in self._idf.idfobjects[self._FILE_SCHEDULE]:
         if schedule.Name == f'{schedule_type} schedules {usage}':
           return
-      file_name = self._write_schedules_file(usage, new_schedules[0])
+      file_name = self._write_schedules_file(new_schedules[0], usage)
       self._add_file_schedule(usage, new_schedules[0], file_name)
       return
 
@@ -321,7 +325,7 @@ class Idf:
         if construction.Name == vegetation_name:
           return
       else:
-        if construction.Name == thermal_boundary.construction_name:
+        if construction.Name == f'{thermal_boundary.construction_name} {thermal_boundary.parent_surface.type}':
           return
     if thermal_boundary.layers is None:
       for material in self._idf.idfobjects[self._MATERIAL]:
@@ -340,7 +344,8 @@ class Idf:
       for i in range(0, len(layers) - 1):
         _kwargs[f'Layer_{i + 2}'] = layers[i].material_name
     else:
-      _kwargs = {'Name': thermal_boundary.construction_name, 'Outside_Layer': layers[0].material_name}
+      _kwargs = {'Name': f'{thermal_boundary.construction_name} {thermal_boundary.parent_surface.type}',
+                 'Outside_Layer': layers[0].material_name}
       for i in range(1, len(layers) - 1):
         _kwargs[f'Layer_{i + 1}'] = layers[i].material_name
     self._idf.newidfobject(self._CONSTRUCTION, **_kwargs)
@@ -387,9 +392,9 @@ class Idf:
     thermostat = self._add_thermostat(thermal_zone)
     self._idf.newidfobject(self._IDEAL_LOAD_AIR_SYSTEM,
                            Zone_Name=zone_name,
-                           System_Availability_Schedule_Name=f'HVAC AVAIL SCHEDULES {thermal_zone.usage_name}',
-                           Heating_Availability_Schedule_Name=f'HVAC AVAIL SCHEDULES {thermal_zone.usage_name}',
-                           Cooling_Availability_Schedule_Name=f'HVAC AVAIL SCHEDULES {thermal_zone.usage_name}',
+                           System_Availability_Schedule_Name=f'Thermostat_availability schedules {thermal_zone.usage_name}',
+                           Heating_Availability_Schedule_Name=f'Thermostat_availability schedules {thermal_zone.usage_name}',
+                           Cooling_Availability_Schedule_Name=f'Thermostat_availability schedules {thermal_zone.usage_name}',
                            Template_Thermostat_Name=thermostat.Name)
 
   def _add_occupancy(self, thermal_zone, zone_name):
@@ -449,7 +454,7 @@ class Idf:
                            )
 
   def _add_infiltration(self, thermal_zone, zone_name):
-    schedule = f'Infiltration schedules {thermal_zone.usage_name}'
+    schedule = f'INF_CONST schedules {thermal_zone.usage_name}'
     _infiltration = thermal_zone.infiltration_rate_system_off * cte.HOUR_TO_SECONDS
     self._idf.newidfobject(self._INFILTRATION,
                            Name=f'{zone_name}_infiltration',
@@ -459,6 +464,17 @@ class Idf:
                            Air_Changes_per_Hour=_infiltration
                            )
 
+  def _add_infiltration_surface(self, thermal_zone, zone_name):
+    schedule = f'INF_CONST schedules {thermal_zone.usage_name}'
+    _infiltration = thermal_zone.infiltration_rate_area_system_off*cte.INFILTRATION_75PA_TO_4PA
+    self._idf.newidfobject(self._INFILTRATION,
+                           Name=f'{zone_name}_infiltration',
+                           Zone_or_ZoneList_or_Space_or_SpaceList_Name=zone_name,
+                           Schedule_Name=schedule,
+                           Design_Flow_Rate_Calculation_Method='Flow/ExteriorWallArea',
+                           Flow_Rate_per_Exterior_Surface_Area=_infiltration
+                           )
+
   def _add_ventilation(self, thermal_zone, zone_name):
     schedule = f'Ventilation schedules {thermal_zone.usage_name}'
     _air_change = thermal_zone.mechanical_air_change * cte.HOUR_TO_SECONDS
@@ -470,7 +486,7 @@ class Idf:
                            Air_Changes_per_Hour=_air_change
                            )
 
-  def _add_dhw(self, thermal_zone, zone_name):
+  def _add_dhw(self, thermal_zone, zone_name, usage):
     peak_flow_rate = thermal_zone.domestic_hot_water.peak_flow * thermal_zone.total_floor_area
     self._idf.newidfobject(self._DHW,
                            Name=f'DHW {zone_name}',
@@ -478,7 +494,7 @@ class Idf:
                            Flow_Rate_Fraction_Schedule_Name=f'DHW_prof schedules {thermal_zone.usage_name}',
                            Target_Temperature_Schedule_Name=f'DHW_temp schedules {thermal_zone.usage_name}',
                            Hot_Water_Supply_Temperature_Schedule_Name=f'DHW_temp schedules {thermal_zone.usage_name}',
-                           Cold_Water_Supply_Temperature_Schedule_Name=f'cold_temp schedules {zone_name}',
+                           Cold_Water_Supply_Temperature_Schedule_Name=f'cold_temp schedules {usage}',
                            EndUse_Subcategory=f'DHW {zone_name}',
                            Zone_Name=zone_name
                            )
@@ -512,19 +528,25 @@ class Idf:
     self._rename_building(self._city.name)
     self._lod = self._city.level_of_detail.geometry
     for building in self._city.buildings:
+      is_target = building.name in self._target_buildings or building.name in self._adjacent_buildings
       for internal_zone in building.internal_zones:
         if internal_zone.thermal_zones_from_internal_zones is None:
+          self._target_buildings.remoidf_surface_typeve(building.name)
+          is_target = False
           continue
         for thermal_zone in internal_zone.thermal_zones_from_internal_zones:
-          for thermal_boundary in thermal_zone.thermal_boundaries:
 
+          for thermal_boundary in thermal_zone.thermal_boundaries:
             self._add_construction(thermal_boundary)
             if thermal_boundary.parent_surface.vegetation is not None:
               self._add_vegetation_material(thermal_boundary.parent_surface.vegetation)
             for thermal_opening in thermal_boundary.thermal_openings:
               self._add_window_construction_and_material(thermal_opening)
-          usage = thermal_zone.usage_name
-          if building.name in self._target_buildings or building.name in self._adjacent_buildings:
+
+          if is_target:
+            start = datetime.datetime.now()
+            service_temperature = thermal_zone.domestic_hot_water.service_temperature
+            usage = thermal_zone.usage_name
             _new_schedules = self._create_infiltration_schedules(thermal_zone)
             self._add_schedules(usage, 'Infiltration', _new_schedules)
             _new_schedules = self._create_ventilation_schedules(thermal_zone)
@@ -536,12 +558,14 @@ class Idf:
             self._add_schedules(usage, 'Lighting', thermal_zone.lighting.schedules)
             self._add_schedules(usage, 'Appliance', thermal_zone.appliances.schedules)
             self._add_schedules(usage, 'DHW_prof', thermal_zone.domestic_hot_water.schedules)
-            _new_schedules = self._create_yearly_values_schedules('cold_temp',
-                                                                  building.cold_water_temperature[cte.HOUR])
-            self._add_schedules(building.name, 'cold_temp', _new_schedules)
-            value = thermal_zone.domestic_hot_water.service_temperature
-            _new_schedules = self._create_constant_value_schedules('DHW_temp', value)
+            _new_schedules = self._create_yearly_values_schedules('cold_temp', building.cold_water_temperature[cte.HOUR])
+            self._add_schedules(usage, 'cold_temp', _new_schedules)
+            _new_schedules = self._create_constant_value_schedules('DHW_temp', service_temperature)
             self._add_schedules(usage, 'DHW_temp', _new_schedules)
+            _new_schedules = self._create_constant_value_schedules('INF_CONST', 1)
+            self._add_schedules(usage, 'INF_CONST', _new_schedules)
+            _new_schedules = self._create_constant_value_schedules('Thermostat_availability', 1)
+            self._add_schedules(usage, 'Thermostat_availability', _new_schedules)
             _occ = thermal_zone.occupancy
             if _occ.occupancy_density == 0:
               _total_heat = 0
@@ -552,16 +576,18 @@ class Idf:
             self._add_schedules(usage, 'Activity Level', _new_schedules)
             self._add_zone(thermal_zone, building.name)
             self._add_heating_system(thermal_zone, building.name)
-            self._add_infiltration(thermal_zone, building.name)
+            self._add_infiltration_surface(thermal_zone, building.name)
             self._add_ventilation(thermal_zone, building.name)
             self._add_occupancy(thermal_zone, building.name)
             self._add_lighting(thermal_zone, building.name)
             self._add_appliances(thermal_zone, building.name)
-            self._add_dhw(thermal_zone, building.name)
+            self._add_dhw(thermal_zone, building.name, usage)
       if self._export_type == "Surfaces":
-        if building.name in self._target_buildings or building.name in self._adjacent_buildings:
+        if is_target:
           if building.thermal_zones_from_internal_zones is not None:
+            start = datetime.datetime.now()
             self._add_surfaces(building, building.name)
+            print(f'add surfaces {datetime.datetime.now() - start}')
           else:
             self._add_pure_geometry(building, building.name)
         else:
@@ -599,6 +625,18 @@ class Idf:
       Reporting_Frequency="Hourly",
     )
 
+    self._idf.newidfobject(
+      "OUTPUT:VARIABLE",
+      Variable_Name="Zone Air Temperature",
+      Reporting_Frequency="Hourly",
+    )
+
+    self._idf.newidfobject(
+      "OUTPUT:VARIABLE",
+      Variable_Name="Zone Air Relative Humidity",
+      Reporting_Frequency="Hourly",
+    )
+
     # post-process to erase windows associated to adiabatic walls
     windows_list = []
     for window in self._idf.idfobjects[self._WINDOW]:
@@ -717,7 +755,7 @@ class Idf:
         if boundary.parent_surface.vegetation is not None:
           construction_name = f'{boundary.construction_name}_{boundary.parent_surface.vegetation.name}'
         else:
-          construction_name = boundary.construction_name
+          construction_name = f'{boundary.construction_name} {boundary.parent_surface.type}'
         _kwargs['Construction_Name'] = construction_name
 
         surface = self._idf.newidfobject(self._SURFACE, **_kwargs)

hub/exports/building_energy/idf_files/Energy+.idd

@@ -1,4 +1,4 @@
-!IDD_Version 23.2.0
+!IDD_Version 24.1.0
 !IDD_BUILD 7636e6b3e9
 ! ***************************************************************************
 ! This file is the Input Data Dictionary (IDD) for EnergyPlus.
@@ -30002,10 +30002,10 @@ People,
   A7 , \field Mean Radiant Temperature Calculation Type
        \note optional (only required for thermal comfort runs)
        \type choice
-       \key ZoneAveraged
+       \key EnclosureAveraged
        \key SurfaceWeighted
        \key AngleFactor
-       \default ZoneAveraged
+       \default EnclosureAveraged
   A8 , \field Surface Name/Angle Factor List Name
        \type object-list
        \object-list AllHeatTranAngFacNames

hub/exports/building_energy/idf_files/Minimal.idf

@@ -13,7 +13,7 @@
 ! HVAC: None.
 !
 
-  Version,23.2;
+  Version,24.1;
 
   Timestep,4;
 
@@ -154,4 +154,4 @@
   Output:Meter,InteriorLights:Electricity,hourly;
 
   OutputControl:IlluminanceMap:Style,
-     Comma;                                         !- Column separator
+     Comma;                                         !- Column separator

hub/exports/building_energy/insel/insel_monthly_energy_balance.py

@@ -270,7 +270,7 @@ class InselMonthlyEnergyBalance:
         global_irradiance = surface.global_irradiance[cte.MONTH]
         for j in range(0, len(global_irradiance)):
           parameters.append(f'{j + 1} '
-                            f'{global_irradiance[j] * cte.WATTS_HOUR_TO_JULES / 24 / _NUMBER_DAYS_PER_MONTH[j]}')
+                            f'{global_irradiance[j] / 24 / _NUMBER_DAYS_PER_MONTH[j]}')
       else:
         for j in range(0, 12):
           parameters.append(f'{j + 1} 0.0')

hub/exports/energy_building_exports_factory.py

@@ -20,9 +20,10 @@ class EnergyBuildingsExportsFactory:
   """
   Energy Buildings exports factory class
   """
-  def __init__(self, handler, city, path, custom_insel_block='d18599', target_buildings=None):
+  def __init__(self, handler, city, path, custom_insel_block='d18599', target_buildings=None, weather_file=None):
     self._city = city
     self._export_type = '_' + handler.lower()
+    self._weather_file = weather_file
     validate_import_export_type(EnergyBuildingsExportsFactory, handler)
     if isinstance(path, str):
       path = Path(path)
@@ -53,12 +54,13 @@ class EnergyBuildingsExportsFactory:
     """
     idf_data_path = (Path(__file__).parent / './building_energy/idf_files/').resolve()
     url = wh().epw_file(self._city.region_code)
-    weather_path = (Path(__file__).parent.parent / f'data/weather/epw/{url.rsplit("/", 1)[1]}').resolve()
-    if not weather_path.exists():
-      with open(weather_path, 'wb') as epw_file:
+    if self._weather_file is None:
+      self._weather_file = (Path(__file__).parent.parent / f'data/weather/epw/{url.rsplit("/", 1)[1]}').resolve()
+    if not self._weather_file.exists():
+      with open(self._weather_file, 'wb') as epw_file:
         epw_file.write(requests.get(url, allow_redirects=True).content)
-    return Idf(self._city, self._path, (idf_data_path / 'Minimal.idf'), (idf_data_path / 'Energy+.idd'), weather_path,
-               target_buildings=self._target_buildings)
+    return Idf(self._city, self._path, (idf_data_path / 'Minimal.idf'), (idf_data_path / 'Energy+.idd'),
+               self._weather_file, target_buildings=self._target_buildings)
 
   @property
   def _insel_monthly_energy_balance(self):

hub/helpers/constants.py

@@ -24,6 +24,8 @@ BTU_H_TO_WATTS = 0.29307107
 KILO_WATTS_HOUR_TO_JULES = 3600000
 WATTS_HOUR_TO_JULES = 3600
 GALLONS_TO_QUBIC_METERS = 0.0037854117954011185
+INFILTRATION_75PA_TO_4PA = (4/75)**0.65
+
 
 # time
 SECOND = 'second'
@@ -301,6 +303,7 @@ GRID = 'Grid'
 ONSITE_ELECTRICITY = 'Onsite Electricity'
 PHOTOVOLTAIC = 'Photovoltaic'
 BOILER = 'Boiler'
+FURNACE = 'Furnace'
 HEAT_PUMP = 'Heat Pump'
 BASEBOARD = 'Baseboard'
 ELECTRICITY_GENERATOR = 'Electricity generator'
@@ -310,7 +313,8 @@ LATENT = 'Latent'
 LITHIUMION = 'Lithium Ion'
 NICD = 'NiCd'
 LEADACID = 'Lead Acid'
-
+THERMAL = 'thermal'
+ELECTRICAL = 'electrical'
 # Geometry
 EPSILON = 0.0000001
 

hub/helpers/data/montreal_custom_fuel_to_hub_fuel.py

@@ -12,12 +12,16 @@ class MontrealCustomFuelToHubFuel:
   """
   Montreal custom fuel to hub fuel class
   """
+
   def __init__(self):
     self._dictionary = {
-        'gas': cte.GAS,
-        'electricity': cte.ELECTRICITY,
-        'renewable': cte.RENEWABLE
-      }
+      'gas': cte.GAS,
+      'natural gas': cte.GAS,
+      'diesel': cte.DIESEL,
+      'biomass': cte.BIOMASS,
+      'electricity': cte.ELECTRICITY,
+      'renewable': cte.RENEWABLE
+    }
 
   @property
   def dictionary(self) -> dict:

hub/helpers/data/montreal_generation_system_to_hub_energy_generation_system.py

@@ -18,7 +18,7 @@ class MontrealGenerationSystemToHubEnergyGenerationSystem:
       'furnace': cte.BASEBOARD,
       'cooler': cte.CHILLER,
       'electricity generator': cte.ELECTRICITY_GENERATOR,
-      'PV system': cte.PHOTOVOLTAIC,
+      'photovoltaic': cte.PHOTOVOLTAIC,
       'heat pump': cte.HEAT_PUMP
     }
 

hub/imports/construction/eilat_physics_parameters.py

@@ -66,6 +66,8 @@ class EilatPhysicsParameters:
     thermal_archetype.indirect_heated_ratio = 0
     thermal_archetype.infiltration_rate_for_ventilation_system_on = catalog_archetype.infiltration_rate_for_ventilation_system_on
     thermal_archetype.infiltration_rate_for_ventilation_system_off = catalog_archetype.infiltration_rate_for_ventilation_system_off
+    thermal_archetype.infiltration_rate_area_for_ventilation_system_on = catalog_archetype.infiltration_rate_area_for_ventilation_system_on
+    thermal_archetype.infiltration_rate_area_for_ventilation_system_off = catalog_archetype.infiltration_rate_area_for_ventilation_system_off
     effective_thermal_capacity = 0
     _constructions = []
     for catalog_construction in catalog_archetype.constructions:

hub/imports/construction/nrcan_physics_parameters.py

@@ -32,10 +32,21 @@ class NrcanPhysicsParameters:
     city = self._city
     nrcan_catalog = ConstructionCatalogFactory('nrcan').catalog
     for building in city.buildings:
-      if building.function not in Dictionaries().hub_function_to_nrcan_construction_function:
-        logging.error('Building %s has an unknown building function %s', building.name, building.function)
+      main_function = None
+      functions = building.function.split('_')
+      if len(functions) > 1:
+        maximum_percentage = 0
+        for function in functions:
+          percentage_and_function = function.split('-')
+          if float(percentage_and_function[0]) > maximum_percentage:
+            maximum_percentage = float(percentage_and_function[0])
+            main_function = percentage_and_function[-1]
+      else:
+        main_function = functions[-1]
+      if main_function not in Dictionaries().hub_function_to_nrcan_construction_function:
+        logging.error('Building %s has an unknown building function %s', building.name, main_function)
         continue
-      function = Dictionaries().hub_function_to_nrcan_construction_function[building.function]
+      function = Dictionaries().hub_function_to_nrcan_construction_function[main_function]
       try:
         archetype = self._search_archetype(nrcan_catalog, function, building.year_of_construction, self._climate_zone)
 
@@ -67,6 +78,9 @@ class NrcanPhysicsParameters:
     thermal_archetype.indirect_heated_ratio = 0
     thermal_archetype.infiltration_rate_for_ventilation_system_on = catalog_archetype.infiltration_rate_for_ventilation_system_on
     thermal_archetype.infiltration_rate_for_ventilation_system_off = catalog_archetype.infiltration_rate_for_ventilation_system_off
+    thermal_archetype.infiltration_rate_area_for_ventilation_system_on = catalog_archetype.infiltration_rate_area_for_ventilation_system_on
+    thermal_archetype.infiltration_rate_area_for_ventilation_system_off = catalog_archetype.infiltration_rate_area_for_ventilation_system_off
+
     _constructions = []
     for catalog_construction in catalog_archetype.constructions:
       construction = Construction()

hub/imports/construction/nrel_physics_parameters.py

@@ -69,6 +69,8 @@ class NrelPhysicsParameters:
     thermal_archetype.indirect_heated_ratio = catalog_archetype.indirect_heated_ratio
     thermal_archetype.infiltration_rate_for_ventilation_system_on = catalog_archetype.infiltration_rate_for_ventilation_system_on
     thermal_archetype.infiltration_rate_for_ventilation_system_off = catalog_archetype.infiltration_rate_for_ventilation_system_off
+    thermal_archetype.infiltration_rate_area_for_ventilation_system_on = catalog_archetype.infiltration_rate_area_for_ventilation_system_on
+    thermal_archetype.infiltration_rate_area_for_ventilation_system_off = catalog_archetype.infiltration_rate_area_for_ventilation_system_off
     _constructions = []
     for catalog_construction in catalog_archetype.constructions:
       construction = Construction()

hub/imports/energy_systems/montreal_custom_energy_system_parameters.py

@@ -136,10 +136,14 @@ class MontrealCustomEnergySystemParameters:
       _distribution_system.distribution_consumption_variable_flow = \
         archetype_distribution_system.distribution_consumption_variable_flow
       _distribution_system.heat_losses = archetype_distribution_system.heat_losses
-      _emission_system = None
+      _generic_emission_system = None
       if archetype_distribution_system.emission_systems is not None:
-        _emission_system = EmissionSystem()
-      _distribution_system.emission_systems = [_emission_system]
+        _emission_systems = []
+        for emission_system in archetype_distribution_system.emission_systems:
+          _generic_emission_system = EmissionSystem()
+          _generic_emission_system.parasitic_energy_consumption = emission_system.parasitic_energy_consumption
+          _emission_systems.append(_generic_emission_system)
+        _distribution_system.emission_systems = _emission_systems
       _distribution_systems.append(_distribution_system)
     return _distribution_systems
 

hub/imports/energy_systems/montreal_future_energy_systems_parameters.py

@@ -43,6 +43,7 @@ class MontrealFutureEnergySystemParameters:
       archetype_name = building.energy_systems_archetype_name
       try:
         archetype = self._search_archetypes(montreal_custom_catalog, archetype_name)
+        building.energy_systems_archetype_cluster_id = archetype.cluster_id
       except KeyError:
         logging.error('Building %s has unknown energy system archetype for system name %s', building.name,
                       archetype_name)
@@ -87,12 +88,12 @@ class MontrealFutureEnergySystemParameters:
     archetype_generation_systems = archetype_system.generation_systems
     if archetype_generation_systems is not None:
       for archetype_generation_system in archetype_system.generation_systems:
-        if archetype_generation_system.system_type == 'Photovoltaic':
+        if archetype_generation_system.system_type == 'photovoltaic':
           _generation_system = PvGenerationSystem()
           _generation_system.name = archetype_generation_system.name
           _generation_system.model_name = archetype_generation_system.model_name
           _generation_system.manufacturer = archetype_generation_system.manufacturer
-          _type = 'PV system'
+          _type = archetype_generation_system.system_type
           _generation_system.system_type = Dictionaries().montreal_generation_system_to_hub_energy_generation_system[_type]
           _fuel_type = Dictionaries().montreal_custom_fuel_to_hub_fuel[archetype_generation_system.fuel_type]
           _generation_system.fuel_type = _fuel_type
@@ -103,15 +104,21 @@ class MontrealFutureEnergySystemParameters:
           _generation_system.nominal_radiation = archetype_generation_system.nominal_radiation
           _generation_system.standard_test_condition_cell_temperature = archetype_generation_system.standard_test_condition_cell_temperature
           _generation_system.standard_test_condition_maximum_power = archetype_generation_system.standard_test_condition_maximum_power
+          _generation_system.standard_test_condition_radiation = archetype_generation_system.standard_test_condition_radiation
           _generation_system.cell_temperature_coefficient = archetype_generation_system.cell_temperature_coefficient
           _generation_system.width = archetype_generation_system.width
           _generation_system.height = archetype_generation_system.height
           _generation_system.tilt_angle = self._city.latitude
           _generic_storage_system = None
           if archetype_generation_system.energy_storage_systems is not None:
-            _generic_storage_system = ElectricalStorageSystem()
-            _generic_storage_system.type_energy_stored = 'electrical'
-          _generation_system.energy_storage_systems = [_generic_storage_system]
+            _storage_systems = []
+            for storage_system in archetype_generation_system.energy_storage_systems:
+              if storage_system.type_energy_stored == 'electrical':
+                _generic_storage_system = ElectricalStorageSystem()
+                _generic_storage_system.type_energy_stored = 'electrical'
+              _storage_systems.append(_generic_storage_system)
+            _generation_system.energy_storage_systems = _storage_systems
+
         else:
           _generation_system = NonPvGenerationSystem()
           _generation_system.name = archetype_generation_system.name
@@ -119,7 +126,7 @@ class MontrealFutureEnergySystemParameters:
           _generation_system.manufacturer = archetype_generation_system.manufacturer
           _type = archetype_generation_system.system_type
           _generation_system.system_type = Dictionaries().montreal_generation_system_to_hub_energy_generation_system[_type]
-          _fuel_type = Dictionaries().north_america_custom_fuel_to_hub_fuel[archetype_generation_system.fuel_type]
+          _fuel_type = Dictionaries().montreal_custom_fuel_to_hub_fuel[archetype_generation_system.fuel_type]
           _generation_system.fuel_type = _fuel_type
           _generation_system.nominal_heat_output = archetype_generation_system.nominal_heat_output
           _generation_system.nominal_cooling_output = archetype_generation_system.nominal_cooling_output
@@ -185,10 +192,14 @@ class MontrealFutureEnergySystemParameters:
         _distribution_system.distribution_consumption_variable_flow = \
           archetype_distribution_system.distribution_consumption_variable_flow
         _distribution_system.heat_losses = archetype_distribution_system.heat_losses
-        _emission_system = None
+        _generic_emission_system = None
         if archetype_distribution_system.emission_systems is not None:
-          _emission_system = EmissionSystem()
-        _distribution_system.emission_systems = [_emission_system]
+          _emission_systems = []
+          for emission_system in archetype_distribution_system.emission_systems:
+            _generic_emission_system = EmissionSystem()
+            _generic_emission_system.parasitic_energy_consumption = emission_system.parasitic_energy_consumption
+            _emission_systems.append(_generic_emission_system)
+          _distribution_system.emission_systems = _emission_systems
         _distribution_systems.append(_distribution_system)
       return _distribution_systems
 

hub/imports/energy_systems/north_america_custom_energy_system_parameters.py

@@ -1,157 +0,0 @@
-"""
-Energy System catalog heat generation system
-SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2023 Concordia CERC group
-Project Coder Saeed Ranjbar saeed.ranjbar@concordia.ca
-Code contributors: Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
-"""
-
-import logging
-import copy
-
-from hub.catalog_factories.energy_systems_catalog_factory import EnergySystemsCatalogFactory
-from hub.city_model_structure.energy_systems.energy_system import EnergySystem
-from hub.city_model_structure.energy_systems.distribution_system import DistributionSystem
-from hub.city_model_structure.energy_systems.non_pv_generation_system import NonPvGenerationSystem
-from hub.city_model_structure.energy_systems.pv_generation_system import PvGenerationSystem
-from hub.city_model_structure.energy_systems.electrical_storage_system import ElectricalStorageSystem
-from hub.city_model_structure.energy_systems.thermal_storage_system import ThermalStorageSystem
-from hub.city_model_structure.energy_systems.emission_system import EmissionSystem
-from hub.helpers.dictionaries import Dictionaries
-
-
-class NorthAmericaCustomEnergySystemParameters:
-  """
-  MontrealCustomEnergySystemParameters class
-  """
-
-  def __init__(self, city):
-    self._city = city
-
-  def enrich_buildings(self):
-    """
-    Returns the city with the system parameters assigned to the buildings
-    :return:
-    """
-    city = self._city
-    montreal_custom_catalog = EnergySystemsCatalogFactory('north_america').catalog
-    if city.generic_energy_systems is None:
-      _generic_energy_systems = {}
-    else:
-      _generic_energy_systems = city.generic_energy_systems
-    for building in city.buildings:
-      archetype_name = building.energy_systems_archetype_name
-      try:
-        archetype = self._search_archetypes(montreal_custom_catalog, archetype_name)
-      except KeyError:
-        logging.error('Building %s has unknown energy system archetype for system name %s', building.name,
-                      archetype_name)
-        continue
-
-      if archetype.name not in _generic_energy_systems:
-        _generic_energy_systems = self._create_generic_systems_list(archetype, _generic_energy_systems)
-
-    city.generic_energy_systems = _generic_energy_systems
-
-    self._assign_energy_systems_to_buildings(city)
-
-  @staticmethod
-  def _search_archetypes(catalog, name):
-    archetypes = catalog.entries('archetypes')
-    for building_archetype in archetypes:
-      if str(name) == str(building_archetype.name):
-        return building_archetype
-    raise KeyError('archetype not found')
-
-  def _create_generic_systems_list(self, archetype, _generic_energy_systems):
-    building_systems = []
-    for archetype_system in archetype.systems:
-      energy_system = EnergySystem()
-      _hub_demand_types = []
-      for demand_type in archetype_system.demand_types:
-        _hub_demand_types.append(Dictionaries().montreal_demand_type_to_hub_energy_demand_type[demand_type])
-      energy_system.name = archetype_system.name
-      energy_system.demand_types = _hub_demand_types
-      energy_system.configuration_schema = archetype_system.configuration_schema
-      energy_system.generation_systems = self._create_generation_systems(archetype_system)
-      if energy_system.distribution_systems is not None:
-        energy_system.distribution_systems = self._create_distribution_systems(archetype_system)
-      building_systems.append(energy_system)
-
-    _generic_energy_systems[archetype.name] = building_systems
-
-    return _generic_energy_systems
-
-  @staticmethod
-  def _create_generation_systems(archetype_system):
-    _generation_systems = []
-    for archetype_generation_system in archetype_system.generation_systems:
-      if archetype_generation_system.system_type == 'PV system':
-        _generation_system = PvGenerationSystem()
-        _type = 'PV system'
-        _generation_system.system_type = Dictionaries().montreal_generation_system_to_hub_energy_generation_system[_type]
-        _fuel_type = Dictionaries().montreal_custom_fuel_to_hub_fuel[archetype_generation_system.fuel_type]
-        _generation_system.fuel_type = _fuel_type
-        _generation_system.electricity_efficiency = archetype_generation_system.electricity_efficiency
-        _generic_storage_system = None
-        if archetype_generation_system.energy_storage_systems is not None:
-          _generic_storage_system = ElectricalStorageSystem()
-          _generic_storage_system.type_energy_stored = 'electrical'
-        _generation_system.energy_storage_systems = [_generic_storage_system]
-      else:
-        _generation_system = NonPvGenerationSystem()
-        _type = archetype_generation_system.system_type
-        _generation_system.system_type = Dictionaries().montreal_generation_system_to_hub_energy_generation_system[_type]
-        _fuel_type = Dictionaries().north_america_custom_fuel_to_hub_fuel[archetype_generation_system.fuel_type]
-        _generation_system.fuel_type = _fuel_type
-        _generation_system.source_types = archetype_generation_system.source_medium
-        _generation_system.heat_efficiency = archetype_generation_system.heat_efficiency
-        _generation_system.cooling_efficiency = archetype_generation_system.cooling_efficiency
-        _generation_system.electricity_efficiency = archetype_generation_system.electricity_efficiency
-        _generic_storage_system = None
-        if archetype_generation_system.energy_storage_systems is not None:
-          _storage_systems = []
-          for storage_system in archetype_generation_system.energy_storage_systems:
-            if storage_system.type_energy_stored == 'electrical':
-              _generic_storage_system = ElectricalStorageSystem()
-              _generic_storage_system.type_energy_stored = 'electrical'
-            else:
-              _generic_storage_system = ThermalStorageSystem()
-              _generic_storage_system.type_energy_stored = 'thermal'
-            _storage_systems.append(_generic_storage_system)
-          _generation_system.energy_storage_systems = [_storage_systems]
-        if archetype_generation_system.dual_supply_capability:
-          _generation_system.dual_supply_capability = True
-      _generation_systems.append(_generation_system)
-    return _generation_systems
-
-  @staticmethod
-  def _create_distribution_systems(archetype_system):
-    _distribution_systems = []
-    for archetype_distribution_system in archetype_system.distribution_systems:
-      _distribution_system = DistributionSystem()
-      _distribution_system.type = archetype_distribution_system.type
-      _distribution_system.distribution_consumption_fix_flow = \
-        archetype_distribution_system.distribution_consumption_fix_flow
-      _distribution_system.distribution_consumption_variable_flow = \
-        archetype_distribution_system.distribution_consumption_variable_flow
-      _distribution_system.heat_losses = archetype_distribution_system.heat_losses
-      _emission_system = None
-      if archetype_distribution_system.emission_systems is not None:
-        _emission_system = EmissionSystem()
-      _distribution_system.emission_systems = [_emission_system]
-      _distribution_systems.append(_distribution_system)
-    return _distribution_systems
-
-  @staticmethod
-  def _assign_energy_systems_to_buildings(city):
-    for building in city.buildings:
-      _building_energy_systems = []
-      energy_systems_cluster_name = building.energy_systems_archetype_name
-      if str(energy_systems_cluster_name) == 'nan':
-        break
-      _generic_building_energy_systems = city.generic_energy_systems[energy_systems_cluster_name]
-      for _generic_building_energy_system in _generic_building_energy_systems:
-        _building_energy_systems.append(copy.deepcopy(_generic_building_energy_system))
-
-      building.energy_systems = _building_energy_systems

hub/imports/energy_systems_factory.py

@@ -6,17 +6,15 @@ Project Coder Pilar Monsalvete pilar.monsalvete@concordi.
 Code contributors: Peter Yefi peteryefi@gmail.com
 """
 from pathlib import Path
-
 from hub.helpers.utils import validate_import_export_type
 from hub.imports.energy_systems.montreal_custom_energy_system_parameters import MontrealCustomEnergySystemParameters
-from hub.imports.energy_systems.north_america_custom_energy_system_parameters import NorthAmericaCustomEnergySystemParameters
 from hub.imports.energy_systems.montreal_future_energy_systems_parameters import MontrealFutureEnergySystemParameters
 
+
 class EnergySystemsFactory:
   """
   EnergySystemsFactory class
   """
-
   def __init__(self, handler, city, base_path=None):
     if base_path is None:
       base_path = Path(Path(__file__).parent.parent / 'data/energy_systems')
@@ -34,15 +32,6 @@ class EnergySystemsFactory:
     for building in self._city.buildings:
       building.level_of_detail.energy_systems = 1
 
-  def _north_america(self):
-    """
-    Enrich the city by using north america custom energy systems catalog information
-    """
-    NorthAmericaCustomEnergySystemParameters(self._city).enrich_buildings()
-    self._city.level_of_detail.energy_systems = 2
-    for building in self._city.buildings:
-      building.level_of_detail.energy_systems = 2
-
   def _montreal_future(self):
     """
     Enrich the city by using north america custom energy systems catalog information

hub/imports/geometry/geojson.py

@@ -127,6 +127,27 @@ class Geojson:
         function = None
         if self._function_field is not None:
           function = str(feature['properties'][self._function_field])
+          if function == 'Mixed use' or function == 'mixed use':
+            function_parts = []
+            if 'usages' in feature['properties']:
+              usages = feature['properties']['usages']
+              for usage in usages:
+                if self._function_to_hub is not None and usage['usage'] in self._function_to_hub:
+                  function_parts.append(f"{usage['percentage']}-{self._function_to_hub[usage['usage']]}")
+                else:
+                  function_parts.append(f"{usage['percentage']}-{usage['usage']}")
+            else:
+              for key, value in feature['properties'].items():
+                if key.startswith("mixed_type_") and not key.endswith("_percentage"):
+                  type_key = key
+                  percentage_key = f"{key}_percentage"
+                  if percentage_key in feature['properties']:
+                    if self._function_to_hub is not None and feature['properties'][type_key] in self._function_to_hub:
+                      usage_function = self._function_to_hub[feature['properties'][type_key]]
+                      function_parts.append(f"{feature['properties'][percentage_key]}-{usage_function}")
+                    else:
+                      function_parts.append(f"{feature['properties'][percentage_key]}-{feature['properties'][type_key]}")
+            function = "_".join(function_parts)
           if self._function_to_hub is not None:
             # use the transformation dictionary to retrieve the proper function
             if function in self._function_to_hub:
@@ -135,6 +156,8 @@ class Geojson:
         building_aliases = []
         if 'id' in feature:
           building_name = feature['id']
+        elif 'id' in feature['properties']:
+          building_name = feature['properties']['id']
         else:
           building_name = uuid.uuid4()
         if self._aliases_field is not None:

hub/imports/results/simplified_radiosity_algorithm.py

@@ -34,7 +34,7 @@ class SimplifiedRadiosityAlgorithm:
     for key in self._results:
       _irradiance = {}
       header_name = key.split(':')
-      result = [x * cte.WATTS_HOUR_TO_JULES for x in self._results[key]]
+      result = [x for x in self._results[key]]
       city_object_name = header_name[1]
       building = self._city.city_object(city_object_name)
       surface_id = header_name[2]

hub/imports/usage/comnet_usage_parameters.py

@@ -35,29 +35,60 @@ class ComnetUsageParameters:
     city = self._city
     comnet_catalog = UsageCatalogFactory('comnet').catalog
     for building in city.buildings:
-      usage_name = Dictionaries().hub_usage_to_comnet_usage[building.function]
-      try:
-        archetype_usage = self._search_archetypes(comnet_catalog, usage_name)
-      except KeyError:
-        logging.error('Building %s has unknown usage archetype for usage %s', building.name, usage_name)
-        continue
-
-      for internal_zone in building.internal_zones:
-        if internal_zone.area is None:
-          raise TypeError('Internal zone area not defined, ACH cannot be calculated')
-        if internal_zone.volume is None:
-          raise TypeError('Internal zone volume not defined, ACH cannot be calculated')
-        if internal_zone.area <= 0:
-          raise TypeError('Internal zone area is zero, ACH cannot be calculated')
-        volume_per_area = internal_zone.volume / internal_zone.area
-        usage = Usage()
-        usage.name = usage_name
-        self._assign_values(usage, archetype_usage, volume_per_area, building.cold_water_temperature)
-        usage.percentage = 1
-        self._calculate_reduced_values_from_extended_library(usage, archetype_usage)
-
-        internal_zone.usages = [usage]
+      usages = []
+      comnet_archetype_usages = []
+      building_functions = building.function.split('_')
+      for function in building_functions:
+        usages.append(function.split('-'))
+      for usage in usages:
+        comnet_usage_name = Dictionaries().hub_usage_to_comnet_usage[usage[-1]]
+        try:
+          comnet_archetype_usage = self._search_archetypes(comnet_catalog, comnet_usage_name)
+          comnet_archetype_usages.append(comnet_archetype_usage)
+        except KeyError:
+          logging.error('Building %s has unknown usage archetype for usage %s', building.name, comnet_usage_name)
+          continue
+      for (i, internal_zone) in enumerate(building.internal_zones):
+        internal_zone_usages = []
+        if len(building.internal_zones) > 1:
+          volume_per_area = 0
+          if internal_zone.area is None:
+            logging.error('Building %s has internal zone area not defined, ACH cannot be calculated for usage %s',
+                          building.name, usages[i][-1])
+            continue
+          if internal_zone.volume is None:
+            logging.error('Building %s has internal zone volume not defined, ACH cannot be calculated for usage %s',
+                          building.name, usages[i][-1])
+            continue
+          if internal_zone.area <= 0:
+            logging.error('Building %s has internal zone area equal to 0, ACH cannot be calculated for usage %s',
+                          building.name,  usages[i][-1])
+            continue
+          volume_per_area += internal_zone.volume / internal_zone.area
+          usage = Usage()
+          usage.name = usages[i][-1]
+          self._assign_values(usage, comnet_archetype_usages[i], volume_per_area, building.cold_water_temperature)
+          usage.percentage = 1
+          self._calculate_reduced_values_from_extended_library(usage, comnet_archetype_usages[i])
+          internal_zone_usages.append(usage)
+        else:
+          if building.storeys_above_ground is None:
+            logging.error('Building %s no number of storeys assigned, ACH cannot be calculated for usage %s',
+                          building.name,  usages)
+            continue
+          volume_per_area = building.volume / building.floor_area / building.storeys_above_ground
+          for (j, mixed_usage) in enumerate(usages):
+            usage = Usage()
+            usage.name = mixed_usage[-1]
+            if len(usages) > 1:
+              usage.percentage = float(mixed_usage[0]) / 100
+            else:
+              usage.percentage = 1
+            self._assign_values(usage, comnet_archetype_usages[j], volume_per_area, building.cold_water_temperature)
+            self._calculate_reduced_values_from_extended_library(usage, comnet_archetype_usages[j])
+            internal_zone_usages.append(usage)
 
+        internal_zone.usages = internal_zone_usages
   @staticmethod
   def _search_archetypes(comnet_catalog, usage_name):
     comnet_archetypes = comnet_catalog.entries('archetypes').usages

hub/imports/usage/nrcan_usage_parameters.py

@@ -33,53 +33,72 @@ class NrcanUsageParameters:
     city = self._city
     nrcan_catalog = UsageCatalogFactory('nrcan').catalog
     comnet_catalog = UsageCatalogFactory('comnet').catalog
-
     for building in city.buildings:
-      usage_name = Dictionaries().hub_usage_to_nrcan_usage[building.function]
-      try:
-        archetype_usage = self._search_archetypes(nrcan_catalog, usage_name)
-      except KeyError:
-        logging.error('Building %s has unknown usage archetype for usage %s', building.name, usage_name)
-        continue
+      usages = []
+      nrcan_archetype_usages = []
+      comnet_archetype_usages = []
+      building_functions = building.function.split('_')
+      for function in building_functions:
+        usages.append(function.split('-'))
+      for usage in usages:
+        usage_name = Dictionaries().hub_usage_to_nrcan_usage[usage[-1]]
+        try:
+          archetype_usage = self._search_archetypes(nrcan_catalog, usage_name)
+          nrcan_archetype_usages.append(archetype_usage)
+        except KeyError:
+          logging.error('Building %s has unknown usage archetype for usage %s', building.name, usage_name)
+          continue
+        comnet_usage_name = Dictionaries().hub_usage_to_comnet_usage[usage[-1]]
+        try:
+          comnet_archetype_usage = self._search_archetypes(comnet_catalog, comnet_usage_name)
+          comnet_archetype_usages.append(comnet_archetype_usage)
+        except KeyError:
+          logging.error('Building %s has unknown usage archetype for usage %s', building.name, comnet_usage_name)
+          continue
 
-      comnet_usage_name = Dictionaries().hub_usage_to_comnet_usage[building.function]
-      try:
-        comnet_archetype_usage = self._search_archetypes(comnet_catalog, comnet_usage_name)
-      except KeyError:
-        logging.error('Building %s has unknown usage archetype for usage %s', building.name, comnet_usage_name)
-        continue
-
-      for internal_zone in building.internal_zones:
+      for (i, internal_zone) in enumerate(building.internal_zones):
+        internal_zone_usages = []
         if len(building.internal_zones) > 1:
           volume_per_area = 0
           if internal_zone.area is None:
             logging.error('Building %s has internal zone area not defined, ACH cannot be calculated for usage %s',
-                          building.name, usage_name)
+                          building.name, usages[i][-1])
             continue
           if internal_zone.volume is None:
             logging.error('Building %s has internal zone volume not defined, ACH cannot be calculated for usage %s',
-                          building.name, usage_name)
+                          building.name, usages[i][-1])
             continue
           if internal_zone.area <= 0:
             logging.error('Building %s has internal zone area equal to 0, ACH cannot be calculated for usage %s',
-                          building.name,  usage_name)
+                          building.name,  usages[i][-1])
             continue
           volume_per_area += internal_zone.volume / internal_zone.area
+          usage = Usage()
+          usage.name = usages[i][-1]
+          self._assign_values(usage, nrcan_archetype_usages[i], volume_per_area, building.cold_water_temperature)
+          self._assign_comnet_extra_values(usage, comnet_archetype_usages[i], nrcan_archetype_usages[i].occupancy.occupancy_density)
+          usage.percentage = 1
+          self._calculate_reduced_values_from_extended_library(usage, nrcan_archetype_usages[i])
+          internal_zone_usages.append(usage)
         else:
           if building.storeys_above_ground is None:
             logging.error('Building %s no number of storeys assigned, ACH cannot be calculated for usage %s',
-                          building.name,  usage_name)
+                          building.name,  usages)
             continue
           volume_per_area = building.volume / building.floor_area / building.storeys_above_ground
+          for (j, mixed_usage) in enumerate(usages):
+            usage = Usage()
+            usage.name = mixed_usage[-1]
+            if len(usages) > 1:
+              usage.percentage = float(mixed_usage[0]) / 100
+            else:
+              usage.percentage = 1
+            self._assign_values(usage, nrcan_archetype_usages[j], volume_per_area, building.cold_water_temperature)
+            self._assign_comnet_extra_values(usage, comnet_archetype_usages[j], nrcan_archetype_usages[j].occupancy.occupancy_density)
+            self._calculate_reduced_values_from_extended_library(usage, nrcan_archetype_usages[j])
+            internal_zone_usages.append(usage)
 
-        usage = Usage()
-        usage.name = usage_name
-        self._assign_values(usage, archetype_usage, volume_per_area, building.cold_water_temperature)
-        self._assign_comnet_extra_values(usage, comnet_archetype_usage, archetype_usage.occupancy.occupancy_density)
-        usage.percentage = 1
-        self._calculate_reduced_values_from_extended_library(usage, archetype_usage)
-
-        internal_zone.usages = [usage]
+        internal_zone.usages = internal_zone_usages
 
   @staticmethod
   def _search_archetypes(catalog, usage_name):

hub/persistence/repositories/city.py

@@ -136,3 +136,7 @@ class City(Repository):
     except SQLAlchemyError as err:
       logging.error('Error while fetching city by name %s', err)
       raise SQLAlchemyError from err
+
+  def get_by_id(self, city_id) -> Model:
+    with Session(self.engine) as session:
+      return session.execute(select(Model).where(Model.id == city_id)).first()[0]

input_files/processed_output -single_building.geojson

@@ -1,50 +0,0 @@
-{
-    "type": "FeatureCollection",
-    "name": "lachine_group_mach_buildings",
-    "crs": {
-        "type": "name",
-        "properties": {
-            "name": "urn:ogc:def:crs:OGC:1.3:CRS84"
-        }
-    },
-    "features": [
-        {
-            "type": "Feature",
-            "properties": {
-                "name": "1",
-                "address": "",
-                "function": 1000,
-                "height": 23.29,
-                "year_of_construction": 2023
-            },
-            "geometry": {
-                "type": "Polygon",
-                "coordinates": [
-                    [
-                        [
-                            -73.66557613653009,
-                            45.43551716511939
-                        ],
-                        [
-                            -73.66530891881455,
-                            45.43551716511939
-                        ],
-                        [
-                            -73.66530891881455,
-                            45.43590129058549
-                        ],
-                        [
-                            -73.66557613653009,
-                            45.43590129058549
-                        ],
-                        [
-                            -73.66557613653009,
-                            45.43551716511939
-                        ]
-                    ]
-                ]
-            },
-            "id": 1
-        }
-    ]
-}

main.py

@@ -1,52 +0,0 @@
-import pandas as pd
-from pathlib import Path
-from scripts.ep_run_enrich import energy_plus_workflow
-from hub.imports.geometry_factory import GeometryFactory
-from hub.helpers.dictionaries import Dictionaries
-from hub.imports.construction_factory import ConstructionFactory
-from hub.imports.usage_factory import UsageFactory
-from hub.imports.weather_factory import WeatherFactory
-from scripts import random_assignation
-from hub.imports.energy_systems_factory import EnergySystemsFactory
-from scripts.energy_system_sizing_and_simulation_factory import EnergySystemsSimulationFactory
-from scripts.costs.cost import Cost
-from scripts.costs.constants import SYSTEM_RETROFIT_AND_PV
-# Specify the GeoJSON file path
-file_path = (Path(__file__).parent / 'input_files' / 'processed_output.geojson')
-output_path = (Path(__file__).parent / 'out_files').resolve()
-city = GeometryFactory('geojson',
-                       path=file_path,
-                       height_field='height',
-                       year_of_construction_field='year_of_construction',
-                       function_field='function',
-                       function_to_hub=Dictionaries().montreal_function_to_hub_function).city
-ConstructionFactory('nrcan', city).enrich()
-
-UsageFactory('nrcan', city).enrich()
-WeatherFactory('epw', city).enrich()
-energy_plus_workflow(city)
-random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
-EnergySystemsFactory('montreal_future', city).enrich()
-for building in city.buildings:
-  EnergySystemsSimulationFactory('archetype13', building=building, output_path=output_path).enrich()
-sum_floor_area = 0
-buildings_list = []
-for building in city.buildings:
-  buildings_list.append(building.name)
-df = pd.DataFrame(columns=['building_name', 'total_floor_area', 'investment_cost', 'lc CAPEX'])
-df['building_name'] = buildings_list
-for building in city.buildings:
-  for thermal_zone in building.thermal_zones_from_internal_zones:
-    sum_floor_area += thermal_zone.total_floor_area
-  costs = Cost(building=building, retrofit_scenario=SYSTEM_RETROFIT_AND_PV).life_cycle
-  costs.loc['global_capital_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].to_csv(
-    output_path / f'{building.name}_cc.csv')
-  investment_cost = costs.loc['global_capital_costs',
-                              f'Scenario {SYSTEM_RETROFIT_AND_PV}'].loc[0, 'D3020_heat_and_cooling_generating_systems']
-  lcc_capex = costs.loc['total_capital_costs_systems', f'Scenario {SYSTEM_RETROFIT_AND_PV}']
-  df.loc[df['building_name'] == building.name, 'total_floor_area'] = (
-    building.thermal_zones_from_internal_zones[0].total_floor_area)
-  df.loc[df['building_name'] == building.name, 'investment_cost'] = investment_cost
-  df.loc[df['building_name'] == building.name, 'lc CAPEX'] = lcc_capex
-
-df.to_csv(output_path / 'economic analysis.csv')

pv_assessment.py

@@ -1,73 +0,0 @@
-import pandas as pd
-from scripts.geojson_creator import process_geojson
-from pathlib import Path
-import subprocess
-from hub.imports.geometry_factory import GeometryFactory
-from hub.helpers.dictionaries import Dictionaries
-from hub.imports.construction_factory import ConstructionFactory
-from hub.imports.usage_factory import UsageFactory
-from hub.imports.weather_factory import WeatherFactory
-from hub.imports.results_factory import ResultFactory
-from scripts.solar_angles import CitySolarAngles
-from scripts.ep_run_enrich import energy_plus_workflow
-import hub.helpers.constants as cte
-from hub.exports.exports_factory import ExportsFactory
-from scripts.pv_sizing_and_simulation import PVSizingSimulation
-# Specify the GeoJSON file path
-geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.0005)
-file_path = (Path(__file__).parent / 'input_files' / 'output_buildings.geojson')
-# Specify the output path for the PDF file
-output_path = (Path(__file__).parent / 'out_files').resolve()
-# Create city object from GeoJSON file
-city = GeometryFactory('geojson',
-                       path=file_path,
-                       height_field='height',
-                       year_of_construction_field='year_of_construction',
-                       function_field='function',
-                       function_to_hub=Dictionaries().montreal_function_to_hub_function).city
-# Enrich city data
-ConstructionFactory('nrcan', city).enrich()
-
-UsageFactory('nrcan', city).enrich()
-WeatherFactory('epw', city).enrich()
-ExportsFactory('sra', city, output_path).export()
-sra_path = (output_path / f'{city.name}_sra.xml').resolve()
-subprocess.run(['sra', str(sra_path)])
-ResultFactory('sra', city, output_path).enrich()
-energy_plus_workflow(city)
-solar_angles = CitySolarAngles(city.name,
-                               city.latitude,
-                               city.longitude,
-                               tilt_angle=45,
-                               surface_azimuth_angle=180).calculate
-df = pd.DataFrame()
-df.index = ['yearly lighting (kWh)', 'yearly appliance (kWh)', 'yearly heating (kWh)', 'yearly cooling (kWh)',
-            'yearly dhw (kWh)', 'roof area (m2)', 'used area for pv (m2)', 'number of panels', 'pv production (kWh)']
-for building in city.buildings:
-  ghi = [x / cte.WATTS_HOUR_TO_JULES for x in building.roofs[0].global_irradiance[cte.HOUR]]
-  pv_sizing_simulation = PVSizingSimulation(building,
-                                            solar_angles,
-                                            tilt_angle=45,
-                                            module_height=1,
-                                            module_width=2,
-                                            ghi=ghi)
-  pv_sizing_simulation.pv_output()
-  yearly_lighting = building.lighting_electrical_demand[cte.YEAR][0] / 1000
-  yearly_appliance = building.appliances_electrical_demand[cte.YEAR][0] / 1000
-  yearly_heating = building.heating_demand[cte.YEAR][0] / (3.6e6 * 3)
-  yearly_cooling = building.cooling_demand[cte.YEAR][0] / (3.6e6 * 4.5)
-  yearly_dhw = building.domestic_hot_water_heat_demand[cte.YEAR][0] / 1000
-  roof_area = building.roofs[0].perimeter_area
-  used_roof = pv_sizing_simulation.available_space()
-  number_of_pv_panels = pv_sizing_simulation.total_number_of_panels
-  yearly_pv = building.onsite_electrical_production[cte.YEAR][0] / 1000
-  df[f'{building.name}'] = [yearly_lighting, yearly_appliance, yearly_heating, yearly_cooling, yearly_dhw, roof_area,
-                            used_roof, number_of_pv_panels, yearly_pv]
-
-df.to_csv(output_path / 'pv.csv')
-
-
-
-
-
-

scripts/district_heating_network/directory_manager.py

@@ -0,0 +1,16 @@
+from pathlib import Path
+
+
+class DirectoryManager:
+    def __init__(self, base_path):
+        self.base_path = Path(base_path)
+        self.directories = {}
+
+    def create_directory(self, relative_path):
+        full_path = self.base_path / relative_path
+        full_path.mkdir(parents=True, exist_ok=True)
+        self.directories[relative_path] = full_path
+        return full_path
+
+    def get_directory(self, relative_path):
+        return self.directories.get(relative_path, None)

scripts/district_heating_network/district_heating_factory.py

@@ -0,0 +1,248 @@
+import CoolProp.CoolProp as CP
+import math
+import logging
+import numpy as np
+import csv
+
+
+class DistrictHeatingFactory:
+  """
+  DistrictHeatingFactory class
+
+  This class is responsible for managing the district heating network, including
+  enriching the network graph with building data, calculating flow rates,
+  sizing pipes, and analyzing costs.
+  """
+
+  def __init__(self, city, graph, supply_temperature, return_temperature, simultaneity_factor):
+    """
+    Initialize the DistrictHeatingFactory object.
+
+    :param city: The city object containing buildings and their heating demands.
+    :param graph: The network graph representing the district heating network.
+    :param supply_temperature: The supply temperature of the heating fluid in the network (°C).
+    :param return_temperature: The return temperature of the heating fluid in the network (°C).
+    :param simultaneity_factor: The simultaneity factor used to adjust flow rates for non-building pipes.
+    """
+    self._city = city
+    self._network_graph = graph
+    self._supply_temperature = supply_temperature
+    self._return_temperature = return_temperature
+    self.simultaneity_factor = simultaneity_factor
+    self.fluid = "Water"  # The fluid used in the heating network
+
+  def enrich(self):
+    """
+    Enrich the network graph nodes with the whole building object from the city buildings.
+
+    This method associates each building node in the network graph with its corresponding
+    building object from the city, allowing access to heating demand data during calculations.
+    """
+    for node_id, node_attrs in self._network_graph.nodes(data=True):
+      if node_attrs.get('type') == 'building':
+        building_name = node_attrs.get('name')
+        building_found = False
+        for building in self._city.buildings:
+          if building.name == building_name:
+            self._network_graph.nodes[node_id]['building_obj'] = building
+            building_found = True
+            break
+        if not building_found:
+          logging.error(msg=f"Building with name '{building_name}' not found in city.")
+
+  def calculate_flow_rates(self, A, Gext):
+    """
+    Solve the linear system to find the flow rates in each branch.
+
+    :param A: The incidence matrix representing the network connections.
+    :param Gext: The external flow rates for each node in the network.
+    :return: The calculated flow rates for each edge, or None if an error occurs.
+    """
+    try:
+      G = np.linalg.lstsq(A, Gext, rcond=None)[0]
+      return G
+    except np.linalg.LinAlgError as e:
+      logging.error(f"Error solving the linear system: {e}")
+      return None
+
+  def switch_nodes(self, A, edge_index, node_index, edge):
+    """
+    Switch the in and out nodes for the given edge in the incidence matrix A.
+
+    :param A: The incidence matrix representing the network connections.
+    :param edge_index: The index of edges in the incidence matrix.
+    :param node_index: The index of nodes in the incidence matrix.
+    :param edge: The edge (u, v) to switch.
+    """
+    u, v = edge
+    i = node_index[u]
+    j = node_index[v]
+    k = edge_index[edge]
+    A[i, k], A[j, k] = -A[i, k], -A[j, k]
+
+  def sizing(self):
+    """
+    Calculate the hourly mass flow rates, assign them to the edges, and determine the pipe diameters.
+
+    This method generates the flow rates for each hour, adjusting the incidence matrix as needed to
+    ensure all flow rates are positive. It also applies the simultaneity factor to non-building pipes.
+    """
+    num_nodes = self._network_graph.number_of_nodes()
+    num_edges = self._network_graph.number_of_edges()
+    A = np.zeros((num_nodes, num_edges))  # Initialize incidence matrix
+    node_index = {node: i for i, node in enumerate(self._network_graph.nodes())}
+    edge_index = {edge: i for i, edge in enumerate(self._network_graph.edges())}
+
+    # Initialize mass flow rate attribute for each edge
+    for u, v, data in self._network_graph.edges(data=True):
+      self._network_graph.edges[u, v]['mass_flow_rate'] = {"hour": [], "peak": None}
+
+    # Get the length of the hourly demand for the first building (assuming all buildings have the same length)
+    building = next(iter(self._city.buildings))
+    num_hours = len(building.heating_demand['hour'])
+
+    # Loop through each hour to generate Gext and solve AG = Gext
+    for hour in range(8760):
+      Gext = np.zeros(num_nodes)
+
+      # Calculate the hourly mass flow rates for each edge and fill Gext
+      for edge in self._network_graph.edges(data=True):
+        u, v, data = edge
+        for node in [u, v]:
+          if self._network_graph.nodes[node].get('type') == 'building':
+            building = self._network_graph.nodes[node].get('building_obj')
+            if building and "hour" in building.heating_demand:
+              hourly_demand = building.heating_demand["hour"][hour]  # Get demand for current hour
+              specific_heat_capacity = CP.PropsSI('C', 'T', (self._supply_temperature + self._return_temperature) / 2,
+                                                  'P', 101325, self.fluid)
+              mass_flow_rate = hourly_demand / 3600 / (
+                    specific_heat_capacity * (self._supply_temperature - self._return_temperature))
+              Gext[node_index[node]] += mass_flow_rate
+
+        # Update incidence matrix A
+        i = node_index[u]
+        j = node_index[v]
+        k = edge_index[(u, v)]
+        A[i, k] = 1
+        A[j, k] = -1
+
+      # Solve for G (flow rates)
+      G = self.calculate_flow_rates(A, Gext)
+      if G is None:
+        return
+
+      # Check for negative flow rates and adjust A accordingly
+      iterations = 0
+      max_iterations = num_edges * 2
+      while any(flow_rate < 0 for flow_rate in G) and iterations < max_iterations:
+        for idx, flow_rate in enumerate(G):
+          if flow_rate < 0:
+            G[idx] = -G[idx]  # Invert the sign directly
+        iterations += 1
+
+      # Store the final flow rates in the edges for this hour
+      for idx, (edge, flow_rate) in enumerate(zip(self._network_graph.edges(), G)):
+        u, v = edge
+        if not (self._network_graph.nodes[u].get('type') == 'building' or self._network_graph.nodes[v].get(
+            'type') == 'building'):
+          flow_rate *= self.simultaneity_factor  # Apply simultaneity factor for non-building pipes
+        data = self._network_graph.edges[u, v]
+        data['mass_flow_rate']["hour"].append(flow_rate)  # Append the calculated flow rate
+
+    # Calculate the peak flow rate for each edge
+    for u, v, data in self._network_graph.edges(data=True):
+      data['mass_flow_rate']['peak'] = max(data['mass_flow_rate']['hour'])
+
+  def calculate_diameters_and_costs(self, pipe_data):
+    """
+    Calculate the diameter and costs of the pipes based on the maximum flow rate in each edge.
+
+    :param pipe_data: A list of dictionaries containing pipe specifications, including inner diameters
+                      and costs per meter for different nominal diameters (DN).
+    """
+    for u, v, data in self._network_graph.edges(data=True):
+      flow_rate = data.get('mass_flow_rate', {}).get('peak')
+      if flow_rate is not None:
+        try:
+          # Calculate the density of the fluid
+          density = CP.PropsSI('D', 'T', (self._supply_temperature + self._return_temperature) / 2, 'P', 101325,
+                               self.fluid)
+          velocity = 0.9  # Desired fluid velocity in m/s
+          # Calculate the diameter of the pipe required for the given flow rate
+          diameter = math.sqrt((4 * abs(flow_rate)) / (density * velocity * math.pi)) * 1000  # Convert to mm
+          self._network_graph.edges[u, v]['diameter'] = diameter
+
+          # Match to the closest nominal diameter from the pipe data
+          closest_pipe = self.match_nominal_diameter(diameter, pipe_data)
+          self._network_graph.edges[u, v]['nominal_diameter'] = closest_pipe['DN']
+          self._network_graph.edges[u, v]['cost_per_meter'] = closest_pipe['cost_per_meter']
+        except Exception as e:
+          logging.error(f"Error calculating diameter or matching nominal diameter for edge ({u}, {v}): {e}")
+
+  def match_nominal_diameter(self, diameter, pipe_data):
+    """
+    Match the calculated diameter to the closest nominal diameter.
+
+    :param diameter: The calculated diameter of the pipe (in mm).
+    :param pipe_data: A list of dictionaries containing pipe specifications, including inner diameters
+                      and costs per meter for different nominal diameters (DN).
+    :return: The dictionary representing the pipe with the closest nominal diameter.
+    """
+    closest_pipe = min(pipe_data, key=lambda x: abs(x['inner_diameter'] - diameter))
+    return closest_pipe
+
+  def analyze_costs(self):
+    """
+    Analyze the costs based on the nominal diameters of the pipes.
+
+    This method calculates the total cost of piping for each nominal diameter group
+    and returns a summary of the grouped pipes and the total cost.
+
+    :return: A tuple containing the grouped pipe data and the total cost of piping.
+    """
+    pipe_groups = {}
+    total_cost = 0  # Initialize total cost
+
+    for u, v, data in self._network_graph.edges(data=True):
+      dn = data.get('nominal_diameter')
+      if dn is not None:
+        pipe_length = self._network_graph.edges[u, v].get('length', 1) * 2  # Multiply by 2 for supply and return
+        cost_per_meter = data.get('cost_per_meter', 0)
+
+        if dn not in pipe_groups:
+          pipe_groups[dn] = {
+            'DN': dn,
+            'total_length': 0,
+            'cost_per_meter': cost_per_meter
+          }
+        pipe_groups[dn]['total_length'] += pipe_length
+        group_cost = pipe_length * cost_per_meter
+        total_cost += group_cost  # Add to total cost
+
+    # Calculate total cost for each group
+    for group in pipe_groups.values():
+      group['total_cost'] = group['total_length'] * group['cost_per_meter']
+
+    return pipe_groups, total_cost  # Return both the grouped data and total cost
+
+  def save_pipe_groups_to_csv(self, filename):
+    """
+    Save the pipe groups and their total lengths to a CSV file.
+
+    :param filename: The name of the CSV file to save the data to.
+    """
+    pipe_groups, _ = self.analyze_costs()
+
+    with open(filename, mode='w', newline='') as file:
+      writer = csv.writer(file)
+      # Write the header
+      writer.writerow(["Nominal Diameter (DN)", "Total Length (m)", "Cost per Meter", "Total Cost"])
+
+      # Write the data for each pipe group
+      for group in pipe_groups.values():
+        writer.writerow([
+          group['DN'],
+          group['total_length'],
+          group['cost_per_meter'],
+          group['total_cost']
+        ])

scripts/district_heating_network/district_heating_network_creator.py

@@ -0,0 +1,372 @@
+import json
+import math
+import logging
+import matplotlib.pyplot as plt
+import networkx as nx
+from shapely.geometry import Polygon, Point, LineString
+from typing import List, Tuple
+from rtree import index
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+logging.getLogger("numexpr").setLevel(logging.ERROR)
+
+def haversine(lon1, lat1, lon2, lat2):
+  """
+  Calculate the great-circle distance between two points
+  on the Earth specified by their longitude and latitude.
+  """
+  R = 6371000  # Radius of the Earth in meters
+  phi1 = math.radians(lat1)
+  phi2 = math.radians(lat2)
+  delta_phi = math.radians(lat2 - lat1)
+  delta_lambda = math.radians(lon2 - lon1)
+
+  a = math.sin(delta_phi / 2.0) ** 2 + \
+      math.cos(phi1) * math.cos(phi2) * \
+      math.sin(delta_lambda / 2.0) ** 2
+
+  c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
+  return R * c  # Output distance in meters
+
+class DistrictHeatingNetworkCreator:
+  def __init__(self, buildings_file: str, roads_file: str, central_plant_locations: List[Tuple[float, float]]):
+    """
+    Initialize the class with paths to the buildings and roads data files, and central plant locations.
+
+    :param buildings_file: Path to the GeoJSON file containing building data.
+    :param roads_file: Path to the GeoJSON file containing roads data.
+    :param central_plant_locations: List of tuples containing the coordinates of central plant locations.
+    """
+    if len(central_plant_locations) < 1:
+      raise ValueError("The list of central plant locations must have at least one member.")
+
+    self.buildings_file = buildings_file
+    self.roads_file = roads_file
+    self.central_plant_locations = central_plant_locations
+
+  def run(self) -> nx.Graph:
+    """
+    Main method to execute the district heating network creation process.
+    :return: NetworkX graph with nodes and edges representing the network.
+    """
+    try:
+      self._load_and_process_data()
+      self._find_nearest_roads()
+      self._find_nearest_points()
+      self._break_down_roads()
+      self._create_graph()
+      self._create_mst()
+      self._iteratively_remove_edges()
+      self._add_centroids_to_mst()
+      self._convert_edge_weights_to_meters()
+      self._create_final_network_graph()
+      return self.network_graph
+    except Exception as e:
+      logging.error(f"Error during network creation: {e}")
+      raise
+
+  def _load_and_process_data(self):
+    """
+    Load and process the building and road data.
+    """
+    try:
+      # Load building data
+      with open(self.buildings_file, 'r') as file:
+        city = json.load(file)
+
+      self.centroids = []
+      self.building_names = []
+      self.building_positions = []
+      buildings = city['features']
+      for building in buildings:
+        coordinates = building['geometry']['coordinates'][0]
+        building_polygon = Polygon(coordinates)
+        centroid = building_polygon.centroid
+        self.centroids.append(centroid)
+        self.building_names.append(str(building['id']))
+        self.building_positions.append((centroid.x, centroid.y))
+
+      # Add central plant locations as centroids
+      for i, loc in enumerate(self.central_plant_locations, start=1):
+        centroid = Point(loc)
+        self.centroids.append(centroid)
+        self.building_names.append(f'central_plant_{i}')
+        self.building_positions.append((centroid.x, centroid.y))
+
+      # Load road data
+      with open(self.roads_file, 'r') as file:
+        roads = json.load(file)
+
+      line_features = [feature for feature in roads['features'] if feature['geometry']['type'] == 'LineString']
+
+      self.lines = [LineString(feature['geometry']['coordinates']) for feature in line_features]
+      self.cleaned_lines = [LineString([line.coords[0], line.coords[-1]]) for line in self.lines]
+    except Exception as e:
+      logging.error(f"Error loading and processing data: {e}")
+      raise
+
+  def _find_nearest_roads(self):
+    """
+    Find the nearest road for each building centroid.
+    """
+    try:
+      self.closest_roads = []
+      unique_roads_set = set()
+
+      # Create spatial index for roads
+      idx = index.Index()
+      for pos, line in enumerate(self.cleaned_lines):
+        idx.insert(pos, line.bounds)
+
+      for centroid in self.centroids:
+        min_distance = float('inf')
+        closest_road = None
+        for pos in idx.nearest(centroid.bounds, 10):
+          road = self.cleaned_lines[pos]
+          distance = road.distance(centroid)
+          if distance < min_distance:
+            min_distance = distance
+            closest_road = road
+
+        if closest_road and closest_road.wkt not in unique_roads_set:
+          unique_roads_set.add(closest_road.wkt)
+          self.closest_roads.append(closest_road)
+    except Exception as e:
+      logging.error(f"Error finding nearest roads: {e}")
+      raise
+
+  def _find_nearest_points(self):
+    """
+    Find the nearest point on each closest road for each centroid.
+    """
+
+    def find_nearest_point_on_line(point: Point, line: LineString) -> Point:
+      return line.interpolate(line.project(point))
+
+    try:
+      self.nearest_points = []
+      for centroid in self.centroids:
+        min_distance = float('inf')
+        closest_road = None
+        for road in self.closest_roads:
+          distance = centroid.distance(road)
+          if distance < min_distance:
+            min_distance = distance
+            closest_road = road
+
+        if closest_road:
+          nearest_point = find_nearest_point_on_line(centroid, closest_road)
+          self.nearest_points.append(nearest_point)
+    except Exception as e:
+      logging.error(f"Error finding nearest points: {e}")
+      raise
+
+  def _break_down_roads(self):
+    """
+    Break down roads into segments connecting nearest points.
+    """
+
+    def break_down_roads(closest_roads: List[LineString], nearest_points_list: List[Point]) -> List[LineString]:
+      new_segments = []
+      for road in closest_roads:
+        coords = list(road.coords)
+        points_on_road = [point for point in nearest_points_list if road.distance(point) < 0.000000001]
+        sorted_points = sorted(points_on_road, key=lambda point: road.project(point))
+        sorted_points.insert(0, Point(coords[0]))
+        sorted_points.append(Point(coords[-1]))
+        for i in range(len(sorted_points) - 1):
+          segment = LineString([sorted_points[i], sorted_points[i + 1]])
+          new_segments.append(segment)
+      return new_segments
+
+    try:
+      self.new_segments = break_down_roads(self.closest_roads, self.nearest_points)
+      self.cleaned_lines = [line for line in self.cleaned_lines if line not in self.closest_roads]
+      self.cleaned_lines.extend(self.new_segments)
+    except Exception as e:
+      logging.error(f"Error breaking down roads: {e}")
+      raise
+
+  def _create_graph(self):
+    """
+    Create a NetworkX graph from the cleaned lines.
+    """
+    try:
+      self.G = nx.Graph()
+      for line in self.cleaned_lines:
+        coords = list(line.coords)
+        for i in range(len(coords) - 1):
+          u = coords[i]
+          v = coords[i + 1]
+          self.G.add_edge(u, v, weight=Point(coords[i]).distance(Point(coords[i + 1])))
+    except Exception as e:
+      logging.error(f"Error creating graph: {e}")
+      raise
+
+  def _create_mst(self):
+    """
+    Create a Minimum Spanning Tree (MST) from the graph.
+    """
+
+    def find_paths_between_nearest_points(g: nx.Graph, nearest_points: List[Point]) -> List[Tuple]:
+      edges = []
+      for i, start_point in enumerate(nearest_points):
+        start = (start_point.x, start_point.y)
+        for end_point in nearest_points[i + 1:]:
+          end = (end_point.x, end_point.y)
+          if nx.has_path(g, start, end):
+            path = nx.shortest_path(g, source=start, target=end, weight='weight')
+            path_edges = list(zip(path[:-1], path[1:]))
+            edges.extend((u, v, g[u][v]['weight']) for u, v in path_edges)
+      return edges
+
+    try:
+      edges = find_paths_between_nearest_points(self.G, self.nearest_points)
+      h = nx.Graph()
+      h.add_weighted_edges_from(edges)
+      mst = nx.minimum_spanning_tree(h, weight='weight')
+      final_edges = []
+      for u, v in mst.edges():
+        if nx.has_path(self.G, u, v):
+          path = nx.shortest_path(self.G, source=u, target=v, weight='weight')
+          path_edges = list(zip(path[:-1], path[1:]))
+          final_edges.extend((x, y, self.G[x][y]['weight']) for x, y in path_edges)
+      self.final_mst = nx.Graph()
+      self.final_mst.add_weighted_edges_from(final_edges)
+    except Exception as e:
+      logging.error(f"Error creating MST: {e}")
+      raise
+
+  def _iteratively_remove_edges(self):
+    """
+    Iteratively remove edges that do not have any nearest points and have one end with only one connection.
+    Also remove nodes that don't have any connections and street nodes with only one connection.
+    """
+    nearest_points_tuples = [(point.x, point.y) for point in self.nearest_points]
+
+    def find_edges_to_remove(graph: nx.Graph) -> List[Tuple]:
+      edges_to_remove = []
+      for u, v, d in graph.edges(data=True):
+        if u not in nearest_points_tuples and v not in nearest_points_tuples:
+          if graph.degree(u) == 1 or graph.degree(v) == 1:
+            edges_to_remove.append((u, v, d))
+      return edges_to_remove
+
+    def find_nodes_to_remove(graph: nx.Graph) -> List[Tuple]:
+      nodes_to_remove = []
+      for node in graph.nodes():
+        if graph.degree(node) == 0:
+          nodes_to_remove.append(node)
+      return nodes_to_remove
+
+    try:
+      edges_to_remove = find_edges_to_remove(self.final_mst)
+      self.final_mst_steps = [list(self.final_mst.edges(data=True))]
+
+      while edges_to_remove or find_nodes_to_remove(self.final_mst):
+        self.final_mst.remove_edges_from(edges_to_remove)
+        nodes_to_remove = find_nodes_to_remove(self.final_mst)
+        self.final_mst.remove_nodes_from(nodes_to_remove)
+        edges_to_remove = find_edges_to_remove(self.final_mst)
+        self.final_mst_steps.append(list(self.final_mst.edges(data=True)))
+
+      def find_single_connection_street_nodes(graph: nx.Graph) -> List[Tuple]:
+        single_connection_street_nodes = []
+        for node in graph.nodes():
+          if node not in nearest_points_tuples and graph.degree(node) == 1:
+            single_connection_street_nodes.append(node)
+        return single_connection_street_nodes
+
+      single_connection_street_nodes = find_single_connection_street_nodes(self.final_mst)
+
+      while single_connection_street_nodes:
+        for node in single_connection_street_nodes:
+          neighbors = list(self.final_mst.neighbors(node))
+          self.final_mst.remove_node(node)
+          for neighbor in neighbors:
+            if self.final_mst.degree(neighbor) == 0:
+              self.final_mst.remove_node(neighbor)
+        single_connection_street_nodes = find_single_connection_street_nodes(self.final_mst)
+        self.final_mst_steps.append(list(self.final_mst.edges(data=True)))
+    except Exception as e:
+      logging.error(f"Error iteratively removing edges: {e}")
+      raise
+
+  def _add_centroids_to_mst(self):
+    """
+    Add centroids to the final MST graph and connect them to their associated node on the graph.
+    """
+    try:
+      for i, centroid in enumerate(self.centroids):
+        building_name = self.building_names[i]
+        pos = (centroid.x, centroid.y)
+        node_type = 'building' if 'central_plant' not in building_name else 'generation'
+        self.final_mst.add_node(pos, type=node_type, name=building_name, pos=pos)
+
+        nearest_point = None
+        min_distance = float('inf')
+        for node in self.final_mst.nodes():
+          if self.final_mst.nodes[node].get('type') != 'building' and self.final_mst.nodes[node].get('type') != 'generation':
+            distance = centroid.distance(Point(node))
+            if distance < min_distance:
+              min_distance = distance
+              nearest_point = node
+
+        if nearest_point:
+          self.final_mst.add_edge(pos, nearest_point, weight=min_distance)
+    except Exception as e:
+      logging.error(f"Error adding centroids to MST: {e}")
+      raise
+
+  def _convert_edge_weights_to_meters(self):
+    """
+    Convert all edge weights in the final MST graph to meters using the Haversine formula.
+    """
+    try:
+      for u, v, data in self.final_mst.edges(data=True):
+        distance = haversine(u[0], u[1], v[0], v[1])
+        self.final_mst[u][v]['weight'] = distance
+    except Exception as e:
+      logging.error(f"Error converting edge weights to meters: {e}")
+      raise
+
+  def _create_final_network_graph(self):
+    """
+    Create the final network graph with the required attributes from the final MST.
+    """
+    self.network_graph = nx.Graph()
+    node_id = 1
+    node_mapping = {}
+    for node in self.final_mst.nodes:
+      pos = node
+      if 'type' in self.final_mst.nodes[node]:
+        if self.final_mst.nodes[node]['type'] == 'building':
+          name = self.final_mst.nodes[node]['name']
+          node_type = 'building'
+        elif self.final_mst.nodes[node]['type'] == 'generation':
+          name = self.final_mst.nodes[node]['name']
+          node_type = 'generation'
+      else:
+        name = f'junction_{node_id}'
+        node_type = 'junction'
+      self.network_graph.add_node(node_id, name=name, type=node_type, pos=pos)
+      node_mapping[node] = node_id
+      node_id += 1
+    for u, v, data in self.final_mst.edges(data=True):
+      u_new = node_mapping[u]
+      v_new = node_mapping[v]
+      length = data['weight']
+      self.network_graph.add_edge(u_new, v_new, length=length)
+
+  def plot_network_graph(self):
+    """
+    Plot the network graph using matplotlib and networkx.
+    """
+    plt.figure(figsize=(15, 10))
+    pos = {node: data['pos'] for node, data in self.network_graph.nodes(data=True)}
+    nx.draw_networkx_nodes(self.network_graph, pos, node_color='blue', node_size=50)
+    nx.draw_networkx_edges(self.network_graph, pos, edge_color='gray')
+    plt.title('District Heating Network Graph')
+    plt.axis('off')
+    plt.show()

scripts/district_heating_network/road_processor.py

@@ -0,0 +1,56 @@
+from pathlib import Path
+from shapely.geometry import Polygon, Point, shape
+import json
+
+
+def road_processor(x, y, diff):
+    """
+    Processes a .JSON file to find roads that have at least one node within a specified polygon.
+
+    Parameters:
+    x (float): The x-coordinate of the center of the selection box.
+    y (float): The y-coordinate of the center of the selection box.
+    diff (float): The half-width of the selection box.
+
+    Returns:
+    str: The file path of the output GeoJSON file containing the selected roads.
+    """
+    diff += 2 * diff
+    # Define the selection polygon
+    selection_box = Polygon([
+        [x + diff, y - diff],
+        [x - diff, y - diff],
+        [x - diff, y + diff],
+        [x + diff, y + diff]
+    ])
+
+    # Define input and output file paths
+    geojson_file = Path("./input_files/roads.json").resolve()
+    output_file = Path('./input_files/output_roads.geojson').resolve()
+
+    # Initialize a list to store the roads in the region
+    roads_in_region = []
+
+    # Read the GeoJSON file
+    with open(geojson_file, 'r') as file:
+        roads = json.load(file)
+        line_features = [feature for feature in roads['features'] if feature['geometry']['type'] == 'LineString']
+
+        # Check each road feature
+        for feature in line_features:
+            road_shape = shape(feature['geometry'])
+            # Check if any node of the road is inside the selection box
+            if any(selection_box.contains(Point(coord)) for coord in road_shape.coords):
+                roads_in_region.append(feature)
+
+    # Create a new GeoJSON structure with the selected roads
+    output_geojson = {
+        "type": "FeatureCollection",
+        "features": roads_in_region
+    }
+
+    # Write the selected roads to the output file
+    with open(output_file, 'w') as outfile:
+        json.dump(output_geojson, outfile)
+
+    return output_file

scripts/energy_system_analysis_report.py

@@ -1,338 +0,0 @@
-import os
-import hub.helpers.constants as cte
-import matplotlib.pyplot as plt
-import random
-import matplotlib.colors as mcolors
-from matplotlib import cm
-from scripts.report_creation import LatexReport
-
-class EnergySystemAnalysisReport:
-  def __init__(self, city, output_path):
-    self.city = city
-    self.output_path = output_path
-    self.content = []
-    self.report = LatexReport('energy_system_analysis_report.tex')
-
-  def building_energy_info(self):
-
-    table_data = [
-      ["Building Name", "Year of Construction", "function", "Yearly Heating Demand (MWh)",
-       "Yearly Cooling Demand (MWh)", "Yearly DHW Demand (MWh)", "Yearly Electricity Demand (MWh)"]
-    ]
-    intensity_table_data = [["Building Name", "Total Floor Area m2", "Heating Demand Intensity kWh/m2",
-                             "Cooling Demand Intensity kWh/m2", "Electricity Intensity kWh/m2"]]
-
-    for building in self.city.buildings:
-      total_floor_area = 0
-      for zone in building.thermal_zones_from_internal_zones:
-        total_floor_area += zone.total_floor_area
-      building_data = [
-        building.name,
-        str(building.year_of_construction),
-        building.function,
-        str(format(building.heating_demand[cte.YEAR][0] / 3.6e9, '.2f')),
-        str(format(building.cooling_demand[cte.YEAR][0] / 3.6e9, '.2f')),
-        str(format(building.domestic_hot_water_heat_demand[cte.YEAR][0] / 1e6, '.2f')),
-        str(format(
-          (building.lighting_electrical_demand[cte.YEAR][0] + building.appliances_electrical_demand[cte.YEAR][0])
-          / 1e6, '.2f')),
-      ]
-      intensity_data = [
-        building.name,
-        str(format(total_floor_area, '.2f')),
-        str(format(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area), '.2f')),
-        str(format(building.cooling_demand[cte.YEAR][0] / (3.6e6 * total_floor_area), '.2f')),
-        str(format(
-          (building.lighting_electrical_demand[cte.YEAR][0] + building.appliances_electrical_demand[cte.YEAR][0]) /
-          (1e3 * total_floor_area), '.2f'))
-      ]
-      table_data.append(building_data)
-      intensity_table_data.append(intensity_data)
-
-    self.report.add_table(table_data, caption='City Buildings Energy Demands')
-    self.report.add_table(intensity_table_data, caption='Energy Intensity Information')
-
-  def base_case_charts(self):
-    save_directory = self.output_path
-
-    def autolabel(bars, ax):
-      for bar in bars:
-        height = bar.get_height()
-        ax.annotate('{:.1f}'.format(height),
-                    xy=(bar.get_x() + bar.get_width() / 2, height),
-                    xytext=(0, 3),  # 3 points vertical offset
-                    textcoords="offset points",
-                    ha='center', va='bottom')
-
-    def create_hvac_demand_chart(building_names, yearly_heating_demand, yearly_cooling_demand):
-      fig, ax = plt.subplots()
-      bar_width = 0.35
-      index = range(len(building_names))
-
-      bars1 = ax.bar(index, yearly_heating_demand, bar_width, label='Yearly Heating Demand (MWh)')
-      bars2 = ax.bar([i + bar_width for i in index], yearly_cooling_demand, bar_width,
-                     label='Yearly Cooling Demand (MWh)')
-
-      ax.set_xlabel('Building Name')
-      ax.set_ylabel('Energy Demand (MWh)')
-      ax.set_title('Yearly HVAC Demands')
-      ax.set_xticks([i + bar_width / 2 for i in index])
-      ax.set_xticklabels(building_names, rotation=45, ha='right')
-      ax.legend()
-      autolabel(bars1, ax)
-      autolabel(bars2, ax)
-      fig.tight_layout()
-      plt.savefig(save_directory / 'hvac_demand_chart.jpg')
-      plt.close()
-
-    def create_bar_chart(title, ylabel, data, filename, bar_color=None):
-      fig, ax = plt.subplots()
-      bar_width = 0.35
-      index = range(len(building_names))
-
-      if bar_color is None:
-        # Generate a random color
-        bar_color = random.choice(list(mcolors.CSS4_COLORS.values()))
-
-      bars = ax.bar(index, data, bar_width, label=ylabel, color=bar_color)
-
-      ax.set_xlabel('Building Name')
-      ax.set_ylabel('Energy Demand (MWh)')
-      ax.set_title(title)
-      ax.set_xticks([i + bar_width / 2 for i in index])
-      ax.set_xticklabels(building_names, rotation=45, ha='right')
-      ax.legend()
-      autolabel(bars, ax)
-      fig.tight_layout()
-      plt.savefig(save_directory / filename)
-      plt.close()
-
-    building_names = [building.name for building in self.city.buildings]
-    yearly_heating_demand = [building.heating_demand[cte.YEAR][0] / 3.6e9 for building in self.city.buildings]
-    yearly_cooling_demand = [building.cooling_demand[cte.YEAR][0] / 3.6e9 for building in self.city.buildings]
-    yearly_dhw_demand = [building.domestic_hot_water_heat_demand[cte.YEAR][0] / 1e6 for building in
-                         self.city.buildings]
-    yearly_electricity_demand = [(building.lighting_electrical_demand[cte.YEAR][0] +
-                                  building.appliances_electrical_demand[cte.YEAR][0]) / 1e6 for building in
-                                 self.city.buildings]
-
-    create_hvac_demand_chart(building_names, yearly_heating_demand, yearly_cooling_demand)
-    create_bar_chart('Yearly DHW Demands', 'Energy Demand (MWh)', yearly_dhw_demand, 'dhw_demand_chart.jpg', )
-    create_bar_chart('Yearly Electricity Demands', 'Energy Demand (MWh)', yearly_electricity_demand,
-                     'electricity_demand_chart.jpg')
-
-  def maximum_monthly_hvac_chart(self):
-    save_directory = self.output_path
-    months = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October',
-              'November', 'December']
-    for building in self.city.buildings:
-      maximum_monthly_heating_load = []
-      maximum_monthly_cooling_load = []
-      fig, axs = plt.subplots(1, 2, figsize=(12, 6))  # Create a figure with 2 subplots side by side
-      for demand in building.heating_peak_load[cte.MONTH]:
-        maximum_monthly_heating_load.append(demand / 3.6e6)
-      for demand in building.cooling_peak_load[cte.MONTH]:
-        maximum_monthly_cooling_load.append(demand / 3.6e6)
-
-      # Plot maximum monthly heating load
-      axs[0].bar(months, maximum_monthly_heating_load, color='red')  # Plot on the first subplot
-      axs[0].set_title('Maximum Monthly Heating Load')
-      axs[0].set_xlabel('Month')
-      axs[0].set_ylabel('Load (kWh)')
-      axs[0].tick_params(axis='x', rotation=45)
-
-      # Plot maximum monthly cooling load
-      axs[1].bar(months, maximum_monthly_cooling_load, color='blue')  # Plot on the second subplot
-      axs[1].set_title('Maximum Monthly Cooling Load')
-      axs[1].set_xlabel('Month')
-      axs[1].set_ylabel('Load (kWh)')
-      axs[1].tick_params(axis='x', rotation=45)
-
-      plt.tight_layout()  # Adjust layout to prevent overlapping
-      plt.savefig(save_directory / f'{building.name}_monthly_maximum_hvac_loads.jpg')
-      plt.close()
-
-  def load_duration_curves(self):
-    save_directory = self.output_path
-    for building in self.city.buildings:
-      heating_demand = [demand / 3.6e6 for demand in building.heating_demand[cte.HOUR]]
-      cooling_demand = [demand / 3.6e6 for demand in building.cooling_demand[cte.HOUR]]
-      heating_demand_sorted = sorted(heating_demand, reverse=True)
-      cooling_demand_sorted = sorted(cooling_demand, reverse=True)
-
-      plt.style.use('ggplot')
-
-      # Create figure and axis objects with 1 row and 2 columns
-      fig, axs = plt.subplots(1, 2, figsize=(12, 6))
-
-      # Plot sorted heating demand
-      axs[0].plot(heating_demand_sorted, color='red', linewidth=2, label='Heating Demand')
-      axs[0].set_xlabel('Hour', fontsize=14)
-      axs[0].set_ylabel('Heating Demand (kWh)', fontsize=14)
-      axs[0].set_title('Heating Load Duration Curve', fontsize=16)
-      axs[0].grid(True)
-      axs[0].legend(loc='upper right', fontsize=12)
-
-      # Plot sorted cooling demand
-      axs[1].plot(cooling_demand_sorted, color='blue', linewidth=2, label='Cooling Demand')
-      axs[1].set_xlabel('Hour', fontsize=14)
-      axs[1].set_ylabel('Cooling Demand (kWh)', fontsize=14)
-      axs[1].set_title('Cooling Load Duration Curve', fontsize=16)
-      axs[1].grid(True)
-      axs[1].legend(loc='upper right', fontsize=12)
-
-      # Adjust layout
-      plt.tight_layout()
-      plt.savefig(save_directory / f'{building.name}_load_duration_curve.jpg')
-      plt.close()
-
-  def individual_building_info(self, building):
-    table_data = [
-      ["Maximum Monthly HVAC Demands",
-       f"\\includegraphics[width=1\\linewidth]{{{building.name}_monthly_maximum_hvac_loads.jpg}}"],
-      ["Load Duration Curve", f"\\includegraphics[width=1\\linewidth]{{{building.name}_load_duration_curve.jpg}}"],
-    ]
-
-    self.report.add_table(table_data, caption=f'{building.name} Information', first_column_width=1.5)
-
-  def building_system_retrofit_results(self, building_name, current_system, new_system):
-    current_system_archetype = current_system[f'{building_name}']['Energy System Archetype']
-    current_system_heating = current_system[f'{building_name}']['Heating Equipments']
-    current_system_cooling = current_system[f'{building_name}']['Cooling Equipments']
-    current_system_dhw = current_system[f'{building_name}']['DHW Equipments']
-    current_system_pv = current_system[f'{building_name}']['Photovoltaic System Capacity']
-    current_system_heating_fuel = current_system[f'{building_name}']['Heating Fuel']
-    current_system_hvac_consumption = current_system[f'{building_name}']['Yearly HVAC Energy Consumption (MWh)']
-    current_system_dhw_consumption = current_system[f'{building_name}']['DHW Energy Consumption (MWH)']
-    current_pv_production = current_system[f'{building_name}']['PV Yearly Production (kWh)']
-    current_capital_cost = current_system[f'{building_name}']['Energy System Capital Cost (CAD)']
-    current_operational = current_system[f'{building_name}']['Energy System Average Yearly Operational Cost (CAD)']
-    current_lcc = current_system[f'{building_name}']['Energy System Life Cycle Cost (CAD)']
-    new_system_archetype = new_system[f'{building_name}']['Energy System Archetype']
-    new_system_heating = new_system[f'{building_name}']['Heating Equipments']
-    new_system_cooling = new_system[f'{building_name}']['Cooling Equipments']
-    new_system_dhw = new_system[f'{building_name}']['DHW Equipments']
-    new_system_pv = new_system[f'{building_name}']['Photovoltaic System Capacity']
-    new_system_heating_fuel = new_system[f'{building_name}']['Heating Fuel']
-    new_system_hvac_consumption = new_system[f'{building_name}']['Yearly HVAC Energy Consumption (MWh)']
-    new_system_dhw_consumption = new_system[f'{building_name}']['DHW Energy Consumption (MWH)']
-    new_pv_production = new_system[f'{building_name}']['PV Yearly Production (kWh)']
-    new_capital_cost = new_system[f'{building_name}']['Energy System Capital Cost (CAD)']
-    new_operational = new_system[f'{building_name}']['Energy System Average Yearly Operational Cost (CAD)']
-    new_lcc = new_system[f'{building_name}']['Energy System Life Cycle Cost (CAD)']
-
-    energy_system_table_data = [
-      ["Detail", "Existing System", "Proposed System"],
-      ["Energy System Archetype", current_system_archetype, new_system_archetype],
-      ["Heating Equipments", current_system_heating, new_system_heating],
-      ["Cooling Equipments", current_system_cooling, new_system_cooling],
-      ["DHW Equipments", current_system_dhw, new_system_dhw],
-      ["Photovoltaic System Capacity", current_system_pv, new_system_pv],
-      ["Heating Fuel", current_system_heating_fuel, new_system_heating_fuel],
-      ["Yearly HVAC Energy Consumption (MWh)", current_system_hvac_consumption, new_system_hvac_consumption],
-      ["DHW Energy Consumption (MWH)", current_system_dhw_consumption, new_system_dhw_consumption],
-      ["PV Yearly Production (kWh)",  current_pv_production, new_pv_production],
-      ["Energy System Capital Cost (CAD)", current_capital_cost, new_capital_cost],
-      ["Energy System Average Yearly Operational Cost (CAD)", current_operational, new_operational],
-      ["Energy System Life Cycle Cost (CAD)", current_lcc, new_lcc]
-    ]
-    self.report.add_table(energy_system_table_data, caption=f'Building {building_name} Energy System Characteristics')
-
-  def building_fuel_consumption_breakdown(self, building):
-    save_directory = self.output_path
-    # Initialize variables to store fuel consumption breakdown
-    fuel_breakdown = {
-      "Heating": {"Gas": 0, "Electricity": 0},
-      "Domestic Hot Water": {"Gas": 0, "Electricity": 0},
-      "Cooling": {"Electricity": 0},
-      "Appliance": building.appliances_electrical_demand[cte.YEAR][0] / 1e6,
-      "Lighting": building.lighting_electrical_demand[cte.YEAR][0] / 1e6
-    }
-
-    # Iterate through energy systems of the building
-    for energy_system in building.energy_systems:
-      for demand_type in energy_system.demand_types:
-        if demand_type == cte.HEATING:
-          consumption = building.heating_consumption[cte.YEAR][0] / 3.6e9
-          for generation_system in energy_system.generation_systems:
-            if generation_system.fuel_type == cte.ELECTRICITY:
-              fuel_breakdown[demand_type]["Electricity"] += consumption
-            else:
-              fuel_breakdown[demand_type]["Gas"] += consumption
-        elif demand_type == cte.DOMESTIC_HOT_WATER:
-          consumption = building.domestic_hot_water_consumption[cte.YEAR][0] / 1e6
-          for generation_system in energy_system.generation_systems:
-            if generation_system.fuel_type == cte.ELECTRICITY:
-              fuel_breakdown[demand_type]["Electricity"] += consumption
-            else:
-              fuel_breakdown[demand_type]["Gas"] += consumption
-        elif demand_type == cte.COOLING:
-          consumption = building.cooling_consumption[cte.YEAR][0] / 3.6e9
-          fuel_breakdown[demand_type]["Electricity"] += consumption
-
-    electricity_labels = ['Appliance', 'Lighting']
-    electricity_sizes = [fuel_breakdown['Appliance'], fuel_breakdown['Lighting']]
-    if fuel_breakdown['Heating']['Electricity'] > 0:
-      electricity_labels.append('Heating')
-      electricity_sizes.append(fuel_breakdown['Heating']['Electricity'])
-    if fuel_breakdown['Cooling']['Electricity'] > 0:
-      electricity_labels.append('Cooling')
-      electricity_sizes.append(fuel_breakdown['Cooling']['Electricity'])
-    if fuel_breakdown['Domestic Hot Water']['Electricity'] > 0:
-      electricity_labels.append('Domestic Hot Water')
-      electricity_sizes.append(fuel_breakdown['Domestic Hot Water']['Electricity'])
-
-    # Data for bar chart
-    gas_labels = ['Heating', 'Domestic Hot Water']
-    gas_sizes = [fuel_breakdown['Heating']['Gas'], fuel_breakdown['Domestic Hot Water']['Gas']]
-
-    # Set the style
-    plt.style.use('ggplot')
-
-    # Create plot grid
-    fig, axs = plt.subplots(1, 2, figsize=(12, 6))
-
-    # Plot pie chart for electricity consumption breakdown
-    colors = cm.get_cmap('tab20c', len(electricity_labels))
-    axs[0].pie(electricity_sizes, labels=electricity_labels,
-               autopct=lambda pct: f"{pct:.1f}%\n({pct / 100 * sum(electricity_sizes):.2f})",
-               startangle=90, colors=[colors(i) for i in range(len(electricity_labels))])
-    axs[0].set_title('Electricity Consumption Breakdown')
-
-    # Plot bar chart for natural gas consumption breakdown
-    colors = cm.get_cmap('Paired', len(gas_labels))
-    axs[1].bar(gas_labels, gas_sizes, color=[colors(i) for i in range(len(gas_labels))])
-    axs[1].set_ylabel('Consumption (MWh)')
-    axs[1].set_title('Natural Gas Consumption Breakdown')
-
-    # Add grid to bar chart
-    axs[1].grid(axis='y', linestyle='--', alpha=0.7)
-
-    # Add a title to the entire figure
-    plt.suptitle('Building Energy Consumption Breakdown', fontsize=16, fontweight='bold')
-
-    # Adjust layout
-    plt.tight_layout()
-
-    # Save the plot as a high-quality image
-    plt.savefig(save_directory / f'{building.name}_energy_consumption_breakdown.png', dpi=300)
-    plt.close()
-
-  def create_report(self, current_system, new_system):
-    os.chdir(self.output_path)
-    self.report.add_section('Current Status')
-    self.building_energy_info()
-    self.base_case_charts()
-    self.report.add_image('hvac_demand_chart.jpg', caption='Yearly HVAC Demands')
-    self.report.add_image('dhw_demand_chart.jpg', caption='Yearly DHW Demands')
-    self.report.add_image('electricity_demand_chart.jpg', caption='Yearly Electricity Demands')
-    self.maximum_monthly_hvac_chart()
-    self.load_duration_curves()
-    for building in self.city.buildings:
-      self.individual_building_info(building)
-      self.building_system_retrofit_results(building_name=building.name, current_system=current_system, new_system=new_system)
-      self.building_fuel_consumption_breakdown(building)
-      self.report.add_image(f'{building.name}_energy_consumption_breakdown.png',
-                            caption=f'Building {building.name} Consumption by source and sector breakdown')
-    self.report.save_report()
-    self.report.compile_to_pdf()

scripts/energy_system_retrofit_results.py

@@ -1,68 +0,0 @@
-import hub.helpers.constants as cte
-
-
-def system_results(buildings):
-  system_performance_summary = {}
-  fields = ["Energy System Archetype", "Heating Equipments", "Cooling Equipments", "DHW Equipments",
-            "Photovoltaic System Capacity", "Heating Fuel", "Yearly HVAC Energy Consumption (MWh)",
-            "DHW Energy Consumption (MWH)", "PV Yearly Production (kWh)", "LCC Analysis Duration (Years)",
-            "Energy System Capital Cost (CAD)", "Energy System Average Yearly Operational Cost (CAD)",
-            "Energy System Life Cycle Cost (CAD)"]
-  for building in buildings:
-    system_performance_summary[f'{building.name}'] = {}
-    for field in fields:
-      system_performance_summary[f'{building.name}'][field] = '-'
-
-  for building in buildings:
-    fuels = []
-    system_performance_summary[f'{building.name}']['Energy System Archetype'] = building.energy_systems_archetype_name
-    energy_systems = building.energy_systems
-    for energy_system in energy_systems:
-      demand_types = energy_system.demand_types
-      for demand_type in demand_types:
-        if demand_type == cte.COOLING:
-          equipments = []
-          for generation_system in energy_system.generation_systems:
-            if generation_system.fuel_type == cte.ELECTRICITY:
-              equipments.append(generation_system.name or generation_system.system_type)
-            cooling_equipments = ", ".join(equipments)
-            system_performance_summary[f'{building.name}']['Cooling Equipments'] = cooling_equipments
-        elif demand_type == cte.HEATING:
-          equipments = []
-          for generation_system in energy_system.generation_systems:
-            if generation_system.nominal_heat_output is not None:
-              equipments.append(generation_system.name or generation_system.system_type)
-              fuels.append(generation_system.fuel_type)
-            heating_equipments = ", ".join(equipments)
-            system_performance_summary[f'{building.name}']['Heating Equipments'] = heating_equipments
-        elif demand_type == cte.DOMESTIC_HOT_WATER:
-          equipments = []
-          for generation_system in energy_system.generation_systems:
-            equipments.append(generation_system.name or generation_system.system_type)
-            dhw_equipments = ", ".join(equipments)
-            system_performance_summary[f'{building.name}']['DHW Equipments'] = dhw_equipments
-      for generation_system in energy_system.generation_systems:
-        if generation_system.system_type == cte.PHOTOVOLTAIC:
-          system_performance_summary[f'{building.name}'][
-            'Photovoltaic System Capacity'] = generation_system.nominal_electricity_output or str(0)
-    heating_fuels = ", ".join(fuels)
-    system_performance_summary[f'{building.name}']['Heating Fuel'] = heating_fuels
-    system_performance_summary[f'{building.name}']['Yearly HVAC Energy Consumption (MWh)'] = format(
-      (building.heating_consumption[cte.YEAR][0] + building.cooling_consumption[cte.YEAR][0]) / 3.6e9, '.2f')
-    system_performance_summary[f'{building.name}']['DHW Energy Consumption (MWH)'] = format(
-      building.domestic_hot_water_consumption[cte.YEAR][0] / 1e6, '.2f')
-  return system_performance_summary
-
-
-def new_system_results(buildings):
-  new_system_performance_summary = {}
-  fields = ["Energy System Archetype", "Heating Equipments", "Cooling Equipments", "DHW Equipments",
-            "Photovoltaic System Capacity", "Heating Fuel", "Yearly HVAC Energy Consumption (MWh)",
-            "DHW Energy Consumption (MWH)", "PV Yearly Production (kWh)", "LCC Analysis Duration (Years)",
-            "Energy System Capital Cost (CAD)", "Energy System Average Yearly Operational Cost (CAD)",
-            "Energy System Life Cycle Cost (CAD)"]
-  for building in buildings:
-    new_system_performance_summary[f'{building.name}'] = {}
-    for field in fields:
-      new_system_performance_summary[f'{building.name}'][field] = '-'
-  return new_system_performance_summary

scripts/energy_system_sizing.py

@@ -1,70 +0,0 @@
-"""
-Energy System rule-based sizing
-SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2023 Concordia CERC group
-Project Coder Saeed Ranjbar saeed.ranjbar@concordia.ca
-"""
-
-import hub.helpers.constants as cte
-
-
-class SystemSizing:
-  """
-  The energy system sizing class
-  """
-  def __init__(self, buildings):
-    self.buildings = buildings
-
-  def hvac_sizing(self):
-    for building in self.buildings:
-      peak_heating_demand = building.heating_peak_load[cte.YEAR][0] / 3600
-      peak_cooling_demand = building.cooling_peak_load[cte.YEAR][0] / 3600
-      if peak_heating_demand > peak_cooling_demand:
-        sizing_demand = peak_heating_demand
-        for system in building.energy_systems:
-          if 'Heating' in system.demand_types:
-            for generation in system.generation_systems:
-              if generation.system_type == 'Heat Pump':
-                if generation.source_medium == cte.AIR:
-                  generation.source_temperature = building.external_temperature
-                generation.nominal_heat_output = 0.6 * sizing_demand / 1000
-                if generation.energy_storage_systems is not None:
-                  for storage in generation.energy_storage_systems:
-                    if storage.type_energy_stored == 'thermal':
-                      storage.volume = building.heating_peak_load[cte.YEAR][0] / (cte.WATER_HEAT_CAPACITY*cte.WATER_DENSITY * 20)
-              elif generation.system_type == 'Boiler':
-                generation.nominal_heat_output = 0.4 * sizing_demand / 1000
-
-      else:
-        sizing_demand = peak_cooling_demand
-        for system in building.energy_systems:
-          if 'Cooling' in system.demand_types:
-            for generation in system.generation_systems:
-              if generation.system_type == 'Heat Pump':
-                generation.nominal_heat_output = sizing_demand / 1000
-
-  def montreal_custom(self):
-    for building in self.buildings:
-      energy_systems = building.energy_systems
-      for energy_system in energy_systems:
-        demand_types = energy_system.demand_types
-        generation_systems = energy_system.generation_systems
-        if cte.HEATING in demand_types:
-          if len(generation_systems) == 1:
-            for generation in generation_systems:
-              generation.nominal_heat_output = building.heating_peak_load[cte.YEAR][0]
-          else:
-            for generation in generation_systems:
-              generation.nominal_heat_output = building.heating_peak_load[cte.YEAR][0] / (len(generation_systems))
-        elif cte.COOLING in demand_types:
-          if len(generation_systems) == 1:
-            for generation in generation_systems:
-              generation.nominal_cooling_output = building.cooling_peak_load[cte.YEAR][0]
-          else:
-            for generation in generation_systems:
-              generation.nominal_heat_output = building.cooling_peak_load[cte.YEAR][0] / (len(generation_systems))
-
-
-
-
-

scripts/ep_workflow.py

@@ -1,63 +0,0 @@
-import glob
-import os
-import sys
-from pathlib import Path
-import csv
-
-from hub.imports.geometry_factory import GeometryFactory
-from hub.imports.construction_factory import ConstructionFactory
-from hub.imports.usage_factory import UsageFactory
-from hub.imports.weather_factory import WeatherFactory
-from hub.exports.energy_building_exports_factory import EnergyBuildingsExportsFactory
-from hub.helpers.dictionaries import Dictionaries
-from hub.imports.results_factory import ResultFactory
-
-sys.path.append('./')
-
-try:
-  file_path = (Path(__file__).parent.parent / 'input_files' / 'eilat.geojson')
-  out_path = (Path(__file__).parent.parent / 'out_files')
-  files = glob.glob(f'{out_path}/*')
-
-  for file in files:
-    if file != '.gitignore':
-      os.remove(file)
-
-  print('[simulation start]')
-  city = GeometryFactory('geojson',
-                         path=file_path,
-                         height_field='heightmax',
-                         year_of_construction_field='ANNEE_CONS',
-                         function_field='CODE_UTILI',
-                         function_to_hub=Dictionaries().eilat_function_to_hub_function).city
-  print(f'city created from {file_path}')
-  ConstructionFactory('eilat', city).enrich()
-  print('enrich constructions... done')
-  UsageFactory('eilat', city).enrich()
-  print('enrich usage... done')
-  WeatherFactory('epw', city).enrich()
-  print('enrich weather... done')
-
-  area = 0
-  volume = 0
-  for building in city.buildings:
-    volume = building.volume
-    for ground in building.grounds:
-      area += ground.perimeter_polygon.area
-
-  print('exporting:')
-  _idf = EnergyBuildingsExportsFactory('idf', city, out_path).export()
-  print('    idf exported...')
-  _idf.run()
-
-  csv_file = str((out_path / f'{city.name}_out.csv').resolve())
-  eso_file = str((out_path / f'{city.name}_out.eso').resolve())
-  idf_file = str((out_path / f'{city.name}.idf').resolve())
-  obj_file = str((out_path / f'{city.name}.obj').resolve())
-  #ResultFactory('energy_plus_multiple_buildings', city, out_path).enrich()
-
-except Exception as ex:
-  print(ex)
-  print('error: ', ex)
-  print('[simulation abort]')
-sys.stdout.flush()

scripts/pv_sizing_and_simulation.py

@@ -1,66 +0,0 @@
-import math
-
-from scripts.radiation_tilted import RadiationTilted
-import hub.helpers.constants as cte
-from hub.helpers.monthly_values import MonthlyValues
-
-
-class PVSizingSimulation(RadiationTilted):
-  def __init__(self, building, solar_angles, tilt_angle, module_height, module_width, ghi):
-    super().__init__(building, solar_angles, tilt_angle, ghi)
-    self.module_height = module_height
-    self.module_width = module_width
-    self.total_number_of_panels = 0
-    self.enrich()
-
-  def available_space(self):
-    roof_area = self.building.roofs[0].perimeter_area
-    maintenance_factor = 0.1
-    orientation_factor = 0.2
-    if self.building.function == cte.RESIDENTIAL:
-      mechanical_equipment_factor = 0.2
-    else:
-      mechanical_equipment_factor = 0.3
-    available_roof = (maintenance_factor + orientation_factor + mechanical_equipment_factor) * roof_area
-    return available_roof
-
-  def inter_row_spacing(self):
-    winter_solstice = self.df[(self.df['AST'].dt.month == 12) &
-                              (self.df['AST'].dt.day == 21) &
-                              (self.df['AST'].dt.hour == 12)]
-    solar_altitude = winter_solstice['solar altitude'].values[0]
-    solar_azimuth = winter_solstice['solar azimuth'].values[0]
-    distance = ((self.module_height * abs(math.cos(math.radians(solar_azimuth)))) /
-                math.tan(math.radians(solar_altitude)))
-    distance = float(format(distance, '.1f'))
-    return distance
-
-  def number_of_panels(self, available_roof, inter_row_distance):
-    space_dimension = math.sqrt(available_roof)
-    space_dimension = float(format(space_dimension, '.2f'))
-    panels_per_row = math.ceil(space_dimension / self.module_width)
-    number_of_rows = math.ceil(space_dimension / inter_row_distance)
-    self.total_number_of_panels = panels_per_row * number_of_rows
-    return panels_per_row, number_of_rows
-
-  def pv_output(self):
-    radiation = self.total_radiation_tilted
-    pv_module_area = self.module_width * self.module_height
-    available_roof = self.available_space()
-    inter_row_spacing = self.inter_row_spacing()
-    self.number_of_panels(available_roof, inter_row_spacing)
-    system_efficiency = 0.2
-    pv_hourly_production = [x * system_efficiency * self.total_number_of_panels * pv_module_area for x in radiation]
-    self.building.onsite_electrical_production[cte.HOUR] = pv_hourly_production
-    self.building.onsite_electrical_production[cte.MONTH] = (
-      MonthlyValues.get_total_month(self.building.onsite_electrical_production[cte.HOUR]))
-    self.building.onsite_electrical_production[cte.YEAR] = [sum(self.building.onsite_electrical_production[cte.MONTH])]
-
-
-
-
-
-
-
-
-

scripts/radiation_tilted.py

@@ -1,113 +0,0 @@
-import pandas as pd
-import math
-import hub.helpers.constants as cte
-from hub.helpers.monthly_values import MonthlyValues
-
-
-class RadiationTilted:
-  def __init__(self, building, solar_angles, tilt_angle, ghi, solar_constant=1366.1, maximum_clearness_index=1,
-               min_cos_zenith=0.065, maximum_zenith_angle=87):
-    self.building = building
-    self.ghi = ghi
-    self.tilt_angle = tilt_angle
-    self.zeniths = solar_angles['zenith'].tolist()[:-1]
-    self.incidents = solar_angles['incident angle'].tolist()[:-1]
-    self.date_time = solar_angles['DateTime'].tolist()[:-1]
-    self.ast = solar_angles['AST'].tolist()[:-1]
-    self.solar_azimuth = solar_angles['solar azimuth'].tolist()[:-1]
-    self.solar_altitude = solar_angles['solar altitude'].tolist()[:-1]
-    data = {'DateTime': self.date_time, 'AST': self.ast, 'solar altitude': self.solar_altitude, 'zenith': self.zeniths,
-            'solar azimuth': self.solar_azimuth, 'incident angle': self.incidents, 'ghi': self.ghi}
-    self.df = pd.DataFrame(data)
-    self.df['DateTime'] = pd.to_datetime(self.df['DateTime'])
-    self.df['AST'] = pd.to_datetime(self.df['AST'])
-    self.df.set_index('DateTime', inplace=True)
-    self.solar_constant = solar_constant
-    self.maximum_clearness_index = maximum_clearness_index
-    self.min_cos_zenith = min_cos_zenith
-    self.maximum_zenith_angle = maximum_zenith_angle
-    self.i_on = []
-    self.i_oh = []
-    self.k_t = []
-    self.fraction_diffuse = []
-    self.diffuse_horizontal = []
-    self.beam_horizontal = []
-    self.dni = []
-    self.beam_tilted = []
-    self.diffuse_tilted = []
-    self.total_radiation_tilted = []
-    self.calculate()
-
-  def dni_extra(self):
-    for i in range(len(self.df)):
-      self.i_on.append(self.solar_constant * (1 + 0.033 * math.cos(math.radians(360 * self.df.index.dayofyear[i] / 365))))
-
-    self.df['extraterrestrial normal radiation (Wh/m2)'] = self.i_on
-
-  def clearness_index(self):
-    for i in range(len(self.df)):
-      self.i_oh.append(self.i_on[i] * max(math.cos(math.radians(self.zeniths[i])), self.min_cos_zenith))
-      self.k_t.append(self.ghi[i] / self.i_oh[i])
-      self.k_t[i] = max(0, self.k_t[i])
-      self.k_t[i] = min(self.maximum_clearness_index, self.k_t[i])
-    self.df['extraterrestrial radiation on horizontal (Wh/m2)'] = self.i_oh
-    self.df['clearness index'] = self.k_t
-
-  def diffuse_fraction(self):
-    for i in range(len(self.df)):
-      if self.k_t[i] <= 0.22:
-        self.fraction_diffuse.append(1 - 0.09 * self.k_t[i])
-      elif self.k_t[i] <= 0.8:
-        self.fraction_diffuse.append(0.9511 - 0.1604 * self.k_t[i] + 4.388 * self.k_t[i] ** 2 -
-                                     16.638 * self.k_t[i] ** 3 + 12.336 * self.k_t[i] ** 4)
-      else:
-        self.fraction_diffuse.append(0.165)
-      if self.zeniths[i] > self.maximum_zenith_angle:
-        self.fraction_diffuse[i] = 1
-
-    self.df['diffuse fraction'] = self.fraction_diffuse
-
-  def radiation_components_horizontal(self):
-    for i in range(len(self.df)):
-      self.diffuse_horizontal.append(self.ghi[i] * self.fraction_diffuse[i])
-      self.beam_horizontal.append(self.ghi[i] - self.diffuse_horizontal[i])
-      self.dni.append((self.ghi[i] - self.diffuse_horizontal[i]) / math.cos(math.radians(self.zeniths[i])))
-      if self.zeniths[i] > self.maximum_zenith_angle or self.dni[i] < 0:
-        self.dni[i] = 0
-
-    self.df['diffuse horizontal (Wh/m2)'] = self.diffuse_horizontal
-    self.df['dni (Wh/m2)'] = self.dni
-    self.df['beam horizontal (Wh/m2)'] = self.beam_horizontal
-
-  def radiation_components_tilted(self):
-    for i in range(len(self.df)):
-      self.beam_tilted.append(self.dni[i] * math.cos(math.radians(self.incidents[i])))
-      self.beam_tilted[i] = max(self.beam_tilted[i], 0)
-      self.diffuse_tilted.append(self.diffuse_horizontal[i] * ((1 + math.cos(math.radians(self.tilt_angle))) / 2))
-      self.total_radiation_tilted.append(self.beam_tilted[i] + self.diffuse_tilted[i])
-
-    self.df['beam tilted (Wh/m2)'] = self.beam_tilted
-    self.df['diffuse tilted (Wh/m2)'] = self.diffuse_tilted
-    self.df['total radiation tilted (Wh/m2)'] = self.total_radiation_tilted
-
-  def calculate(self) -> pd.DataFrame:
-    self.dni_extra()
-    self.clearness_index()
-    self.diffuse_fraction()
-    self.radiation_components_horizontal()
-    self.radiation_components_tilted()
-    return self.df
-
-  def enrich(self):
-    tilted_radiation = self.total_radiation_tilted
-    self.building.roofs[0].global_irradiance_tilted[cte.HOUR] = [x * cte.WATTS_HOUR_TO_JULES for x in
-                                                                 tilted_radiation]
-    self.building.roofs[0].global_irradiance_tilted[cte.HOUR] = [x * cte.WATTS_HOUR_TO_JULES for x in
-                                                                 tilted_radiation]
-    self.building.roofs[0].global_irradiance_tilted[cte.MONTH] = (
-      MonthlyValues.get_total_month(self.building.roofs[0].global_irradiance_tilted[cte.HOUR]))
-    self.building.roofs[0].global_irradiance_tilted[cte.YEAR] = \
-        [sum(self.building.roofs[0].global_irradiance_tilted[cte.MONTH])]
-
-
-

scripts/report_creation.py

@@ -1,73 +0,0 @@
-import subprocess
-import datetime
-
-class LatexReport:
-  def __init__(self, file_name):
-      self.file_name = file_name
-      self.content = []
-      self.content.append(r'\documentclass{article}')
-      self.content.append(r'\usepackage[margin=2.5cm]{geometry}')  # Adjust page margins
-      self.content.append(r'\usepackage{graphicx}')
-      self.content.append(r'\usepackage{tabularx}')
-      self.content.append(r'\begin{document}')
-      # Get current date and time
-      current_datetime = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-      self.content.append(r'\title{Energy System Analysis Report - ' + current_datetime + r'}')
-      self.content.append(r'\author{Next-Generation Cities Institute}')
-      self.content.append(r'\date{}')  # Remove the date field, as it's included in the title now
-      self.content.append(r'\maketitle')
-
-  def add_section(self, section_title):
-      self.content.append(r'\section{' + section_title + r'}')
-
-  def add_subsection(self, subsection_title):
-      self.content.append(r'\subsection{' + subsection_title + r'}')
-
-  def add_text(self, text):
-      self.content.append(text)
-
-  def add_table(self, table_data, caption=None, first_column_width=None):
-    num_columns = len(table_data[0])
-    total_width = 0.9  # Default total width
-
-    if first_column_width is not None:
-      first_column_width_str = str(first_column_width) + 'cm'
-      total_width -= first_column_width / 16.0  # Adjust total width for the first column
-
-    if caption:
-      self.content.append(r'\begin{table}[htbp]')
-      self.content.append(r'\caption{' + caption + r'}')
-      self.content.append(r'\centering')
-
-    self.content.append(r'\begin{tabularx}{\textwidth}{|p{' + first_column_width_str + r'}|' + '|'.join(['X'] * (
-          num_columns - 1)) + '|}' if first_column_width is not None else r'\begin{tabularx}{\textwidth}{|' + '|'.join(
-      ['X'] * num_columns) + '|}')
-    self.content.append(r'\hline')
-    for row in table_data:
-      self.content.append(' & '.join(row) + r' \\')
-      self.content.append(r'\hline')
-    self.content.append(r'\end{tabularx}')
-
-    if caption:
-      self.content.append(r'\end{table}')
-
-  def add_image(self, image_path, caption=None):
-    if caption:
-      self.content.append(r'\begin{figure}[htbp]')
-      self.content.append(r'\centering')
-      self.content.append(r'\includegraphics[width=0.8\textwidth]{' + image_path + r'}')
-      self.content.append(r'\caption{' + caption + r'}')
-      self.content.append(r'\end{figure}')
-    else:
-      self.content.append(r'\begin{figure}[htbp]')
-      self.content.append(r'\centering')
-      self.content.append(r'\includegraphics[width=0.8\textwidth]{' + image_path + r'}')
-      self.content.append(r'\end{figure}')
-
-  def save_report(self):
-    self.content.append(r'\end{document}')  # Add this line to close the document
-    with open(self.file_name, 'w') as f:
-      f.write('\n'.join(self.content))
-
-  def compile_to_pdf(self):
-      subprocess.run(['pdflatex', self.file_name])

scripts/solar_angles.py

@@ -1,146 +0,0 @@
-import math
-import pandas as pd
-from datetime import datetime
-from pathlib import Path
-
-
-class CitySolarAngles:
-  def __init__(self, file_name, location_latitude, location_longitude, tilt_angle, surface_azimuth_angle,
-               standard_meridian=-75):
-    self.file_name = file_name
-    self.location_latitude = location_latitude
-    self.location_longitude = location_longitude
-    self.location_latitude_rad = math.radians(location_latitude)
-    self.surface_azimuth_angle = surface_azimuth_angle
-    self.surface_azimuth_rad = math.radians(surface_azimuth_angle)
-    self.tilt_angle = tilt_angle
-    self.tilt_angle_rad = math.radians(tilt_angle)
-    self.standard_meridian = standard_meridian
-    self.longitude_correction = (location_longitude - standard_meridian) * 4
-    self.timezone = 'Etc/GMT+5'
-
-    self.eot = []
-    self.ast = []
-    self.hour_angles = []
-    self.declinations = []
-    self.solar_altitudes = []
-    self.solar_azimuths = []
-    self.zeniths = []
-    self.incidents = []
-    self.beam_tilted = []
-    self.factor = []
-    self.times = pd.date_range(start='2023-01-01', end='2024-01-01', freq='H', tz=self.timezone)
-    self.df = pd.DataFrame(index=self.times)
-    self.day_of_year = self.df.index.dayofyear
-
-  def solar_time(self, datetime_val, day_of_year):
-    b = (day_of_year - 81) * 2 * math.pi / 364
-    eot = 9.87 * math.sin(2 * b) - 7.53 * math.cos(b) - 1.5 * math.sin(b)
-    self.eot.append(eot)
-
-    # Calculate Local Solar Time (LST)
-    lst_hour = datetime_val.hour
-    lst_minute = datetime_val.minute
-    lst_second = datetime_val.second
-    lst = lst_hour + lst_minute / 60 + lst_second / 3600
-
-    # Calculate Apparent Solar Time (AST) in decimal hours
-    ast_decimal = lst + eot / 60 + self.longitude_correction / 60
-    ast_hours = int(ast_decimal)
-    ast_minutes = round((ast_decimal - ast_hours) * 60)
-
-    # Ensure ast_minutes is within valid range
-    if ast_minutes == 60:
-      ast_hours += 1
-      ast_minutes = 0
-    elif ast_minutes < 0:
-      ast_minutes = 0
-    ast_time = datetime(year=datetime_val.year, month=datetime_val.month, day=datetime_val.day,
-                        hour=ast_hours, minute=ast_minutes)
-    self.ast.append(ast_time)
-    return ast_time
-
-  def declination_angle(self, day_of_year):
-    declination = 23.45 * math.sin(math.radians(360 / 365 * (284 + day_of_year)))
-    declination_radian = math.radians(declination)
-    self.declinations.append(declination)
-    return declination_radian
-
-  def hour_angle(self, ast_time):
-    hour_angle = ((ast_time.hour * 60 + ast_time.minute) - 720) / 4
-    hour_angle_radian = math.radians(hour_angle)
-    self.hour_angles.append(hour_angle)
-    return hour_angle_radian
-
-  def solar_altitude(self, declination_radian, hour_angle_radian):
-    solar_altitude_radians = math.asin(math.cos(self.location_latitude_rad) * math.cos(declination_radian) *
-                                       math.cos(hour_angle_radian) + math.sin(self.location_latitude_rad) *
-                                       math.sin(declination_radian))
-    solar_altitude = math.degrees(solar_altitude_radians)
-    self.solar_altitudes.append(solar_altitude)
-    return solar_altitude_radians
-
-  def zenith(self, solar_altitude_radians):
-    solar_altitude = math.degrees(solar_altitude_radians)
-    zenith_degree = 90 - solar_altitude
-    zenith_radian = math.radians(zenith_degree)
-    self.zeniths.append(zenith_degree)
-    return zenith_radian
-
-  def solar_azimuth_analytical(self, hourangle, declination, zenith):
-    numer = (math.cos(zenith) * math.sin(self.location_latitude_rad) - math.sin(declination))
-    denom = (math.sin(zenith) * math.cos(self.location_latitude_rad))
-    if math.isclose(denom, 0.0, abs_tol=1e-8):
-      cos_azi = 1.0
-    else:
-      cos_azi = numer / denom
-
-    cos_azi = max(-1.0, min(1.0, cos_azi))
-
-    sign_ha = math.copysign(1, hourangle)
-    solar_azimuth_radians = sign_ha * math.acos(cos_azi) + math.pi
-    solar_azimuth_degrees = math.degrees(solar_azimuth_radians)
-    self.solar_azimuths.append(solar_azimuth_degrees)
-    return solar_azimuth_radians
-
-  def incident_angle(self, solar_altitude_radians, solar_azimuth_radians):
-    incident_radian = math.acos(math.cos(solar_altitude_radians) *
-                                math.cos(abs(solar_azimuth_radians - self.surface_azimuth_rad)) *
-                                math.sin(self.tilt_angle_rad) + math.sin(solar_altitude_radians) *
-                                math.cos(self.tilt_angle_rad))
-    incident_angle_degrees = math.degrees(incident_radian)
-    self.incidents.append(incident_angle_degrees)
-    return incident_radian
-
-  @property
-  def calculate(self) -> pd.DataFrame:
-    for i in range(len(self.times)):
-      datetime_val = self.times[i]
-      day_of_year = self.day_of_year[i]
-      declination_radians = self.declination_angle(day_of_year)
-      ast_time = self.solar_time(datetime_val, day_of_year)
-      hour_angle_radians = self.hour_angle(ast_time)
-      solar_altitude_radians = self.solar_altitude(declination_radians, hour_angle_radians)
-      zenith_radians = self.zenith(solar_altitude_radians)
-      solar_azimuth_radians = self.solar_azimuth_analytical(hour_angle_radians, declination_radians, zenith_radians)
-      incident_angle_radian = self.incident_angle(solar_altitude_radians, solar_azimuth_radians)
-
-    self.df['DateTime'] = self.times
-    self.df['AST'] = self.ast
-    self.df['hour angle'] = self.hour_angles
-    self.df['eot'] = self.eot
-    self.df['declination angle'] = self.declinations
-    self.df['solar altitude'] = self.solar_altitudes
-    self.df['zenith'] = self.zeniths
-    self.df['solar azimuth'] = self.solar_azimuths
-    self.df['incident angle'] = self.incidents
-
-    return self.df
-
-
-
-
-
-
-
-

scripts/system_simulation.py

@@ -1,135 +0,0 @@
-import csv
-import math
-from typing import List
-
-from pathlib import Path
-
-import hub.helpers.constants as cte
-from hub.helpers.monthly_values import MonthlyValues
-
-
-class SystemSimulation:
-  def __init__(self, building, out_path):
-    self.building = building
-    self.energy_systems = building.energy_systems
-    self.heating_demand = [0] + building.heating_demand[cte.HOUR]
-    self.cooling_demand = building.cooling_demand
-    self.dhw_demand = building.domestic_hot_water_heat_demand
-    self.T_out = building.external_temperature[cte.HOUR]
-    self.maximum_heating_demand = building.heating_peak_load[cte.YEAR][0]
-    self.maximum_cooling_demand = building.cooling_peak_load[cte.YEAR][0]
-    self.name = building.name
-    self.energy_system_archetype = building.energy_systems_archetype_name
-    self.out_path = out_path
-
-  def archetype1(self):
-    out_path = self.out_path
-    T, T_sup, T_ret, m_ch, m_dis, q_hp, q_aux = [0] * len(self.heating_demand), [0] * len(
-      self.heating_demand), [0] * len(self.heating_demand), [0] * len(self.heating_demand), [0] * len(
-      self.heating_demand), [0] * len(self.heating_demand), [0] * len(self.heating_demand)
-    hp_electricity: List[float] = [0.0] * len(self.heating_demand)
-    aux_fuel: List[float] = [0.0] * len(self.heating_demand)
-    heating_consumption: List[float] = [0.0] * len(self.heating_demand)
-    boiler_consumption: List[float] = [0.0] * len(self.heating_demand)
-    T[0], dt = 25, 3600  # Assuming dt is defined somewhere
-    ua, v, hp_cap, hp_efficiency, boiler_efficiency = 0, 0, 0, 0, 0
-    for energy_system in self.energy_systems:
-      if cte.ELECTRICITY not in energy_system.demand_types:
-        generation_systems = energy_system.generation_systems
-        for generation_system in generation_systems:
-          if generation_system.system_type == cte.HEAT_PUMP and cte.HEATING in energy_system.demand_types:
-            hp_cap = generation_system.nominal_heat_output
-            hp_efficiency = float(generation_system.heat_efficiency)
-            for storage in generation_system.energy_storage_systems:
-              if storage.type_energy_stored == 'thermal':
-                v, h = float(storage.volume), float(storage.height)
-                r_tot = sum(float(layer.thickness) / float(layer.material.conductivity) for layer in
-                            storage.layers)
-                u_tot = 1 / r_tot
-                d = math.sqrt((4 * v) / (math.pi * h))
-                a_side = math.pi * d * h
-                a_top = math.pi * d ** 2 / 4
-                ua = u_tot * (2 * a_top + a_side)
-          elif generation_system.system_type == cte.BOILER:
-            boiler_cap = generation_system.nominal_heat_output
-            boiler_efficiency = float(generation_system.heat_efficiency)
-
-    for i in range(len(self.heating_demand) - 1):
-      T[i + 1] = T[i] + ((m_ch[i] * (T_sup[i] - T[i])) + (
-              ua * (self.T_out[i] - T[i])) / cte.WATER_HEAT_CAPACITY - m_dis[i] * (T[i] - T_ret[i])) * (dt / (cte.WATER_DENSITY * v))
-      if T[i + 1] < 35:
-          q_hp[i + 1] = hp_cap * 1000
-          m_ch[i + 1] = q_hp[i + 1] / (cte.WATER_HEAT_CAPACITY * 7)
-          T_sup[i + 1] = (q_hp[i + 1] / (m_ch[i + 1] * cte.WATER_HEAT_CAPACITY)) + T[i + 1]
-      elif 35 <= T[i + 1] < 45 and q_hp[i] == 0:
-          q_hp[i + 1] = 0
-          m_ch[i + 1] = 0
-          T_sup[i + 1] = T[i + 1]
-      elif 35 <= T[i + 1] < 45 and q_hp[i] > 0:
-          q_hp[i + 1] = hp_cap * 1000
-          m_ch[i + 1] = q_hp[i + 1] / (cte.WATER_HEAT_CAPACITY * 3)
-          T_sup[i + 1] = (q_hp[i + 1] / (m_ch[i + 1] * cte.WATER_HEAT_CAPACITY)) + T[i + 1]
-      else:
-        q_hp[i + 1], m_ch[i + 1], T_sup[i + 1] = 0, 0, T[i + 1]
-
-      hp_electricity[i + 1] = q_hp[i + 1] / hp_efficiency
-      if self.heating_demand[i + 1] == 0:
-        m_dis[i + 1], t_return, T_ret[i + 1] = 0, T[i + 1], T[i + 1]
-      else:
-        if self.heating_demand[i + 1] > 0.5 * self.maximum_heating_demand:
-          factor = 8
-        else:
-          factor = 4
-        m_dis[i + 1] = self.maximum_heating_demand / (cte.WATER_HEAT_CAPACITY * factor * 3600)
-        t_return = T[i + 1] - self.heating_demand[i + 1] / (m_dis[i + 1] * cte.WATER_HEAT_CAPACITY * 3600)
-        if m_dis[i + 1] == 0 or (m_dis[i + 1] > 0 and t_return < 25):
-          T_ret[i + 1] = max(25, T[i + 1])
-        else:
-          T_ret[i + 1] = T[i + 1] - self.heating_demand[i + 1] / (m_dis[i + 1] * cte.WATER_HEAT_CAPACITY * 3600)
-      tes_output = m_dis[i + 1] * cte.WATER_HEAT_CAPACITY * (T[i + 1] - T_ret[i + 1])
-      if tes_output < (self.heating_demand[i + 1] / 3600):
-        q_aux[i + 1] = (self.heating_demand[i + 1] / 3600) - tes_output
-        aux_fuel[i + 1] = (q_aux[i + 1] * dt) / 35.8e6
-      boiler_consumption[i + 1] = q_aux[i + 1] / boiler_efficiency
-      heating_consumption[i + 1] = boiler_consumption[i + 1] + hp_electricity[i + 1]
-    data = list(zip(T, T_sup, T_ret, m_ch, m_dis, q_hp, hp_electricity, aux_fuel, q_aux, self.heating_demand))
-    file_name = f'simulation_results_{self.name}.csv'
-    with open(out_path / file_name, 'w', newline='') as csvfile:
-      output_file = csv.writer(csvfile)
-      # Write header
-      output_file.writerow(['T', 'T_sup', 'T_ret', 'm_ch', 'm_dis', 'q_hp', 'hp_electricity', 'aux_fuel', 'q_aux', 'heating_demand'])
-      # Write data
-      output_file.writerows(data)
-    return heating_consumption, hp_electricity, boiler_consumption, T_sup
-
-  def enrich(self):
-    if self.energy_system_archetype == 'PV+ASHP+GasBoiler+TES' or 'PV+4Pipe+DHW':
-      building_new_heating_consumption, building_heating_electricity_consumption, building_heating_gas_consumption, supply_temperature = (
-        self.archetype1())
-      self.building.heating_consumption[cte.HOUR] = building_new_heating_consumption
-      self.building.heating_consumption[cte.MONTH] = MonthlyValues.get_total_month(self.building.heating_consumption[cte.HOUR])
-      self.building.heating_consumption[cte.YEAR] = [sum(self.building.heating_consumption[cte.MONTH])]
-      disaggregated_consumption = {}
-      for energy_system in self.building.energy_systems:
-        if cte.HEATING in energy_system.demand_types:
-          for generation_system in energy_system.generation_systems:
-            if generation_system.system_type == cte.HEAT_PUMP:
-              generation_system.heat_supply_temperature = supply_temperature
-            disaggregated_consumption[generation_system.fuel_type] = {}
-            if generation_system.fuel_type == cte.ELECTRICITY:
-              disaggregated_consumption[generation_system.fuel_type][
-                cte.HOUR] = building_heating_electricity_consumption
-              disaggregated_consumption[generation_system.fuel_type][cte.MONTH] = MonthlyValues.get_total_month(
-                disaggregated_consumption[generation_system.fuel_type][cte.HOUR])
-              disaggregated_consumption[generation_system.fuel_type][cte.YEAR] = [
-                sum(disaggregated_consumption[generation_system.fuel_type][cte.MONTH])]
-            else:
-              disaggregated_consumption[generation_system.fuel_type][cte.HOUR] = building_heating_gas_consumption
-              disaggregated_consumption[generation_system.fuel_type][cte.MONTH] = MonthlyValues.get_total_month(
-                disaggregated_consumption[generation_system.fuel_type][cte.HOUR])
-              disaggregated_consumption[generation_system.fuel_type][cte.YEAR] = [
-                sum(disaggregated_consumption[generation_system.fuel_type][cte.MONTH])]
-      self.building.heating_fuel_consumption_disaggregated = disaggregated_consumption
-    return self.building
-
-