feat: tests conducted on Lachine

central.py

@@ -1,8 +1,6 @@
 import pandas as pd
 from scripts.geojson_creator import process_geojson
 from pathlib import Path
-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
@@ -13,16 +11,13 @@ 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 SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT_AND_PV
-import hub.helpers.constants as cte
+from scripts.costs.constants import SYSTEM_RETROFIT_AND_PV
 from hub.exports.exports_factory import ExportsFactory
-from scripts.solar_angles import CitySolarAngles
-from scripts.pv_sizing_and_simulation import PVSizingSimulation
-# Specify the GeoJSON file path
 
+# 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' / 'output_buildings.geojson')
+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
@@ -39,37 +34,28 @@ UsageFactory('nrcan', city).enrich()
 WeatherFactory('epw', city).enrich()
 ResultFactory('energy_plus_multiple_buildings', city, output_path).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()
-solar_angles = CitySolarAngles(city.name,
-                               city.latitude,
-                               city.longitude,
-                               tilt_angle=45,
-                               surface_azimuth_angle=180).calculate
 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()
-  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)
-
+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.to_csv(output_path / f'{building.name}_lcc.csv')
   costs.loc['global_capital_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].to_csv(
     output_path / f'{building.name}_cc.csv')
-  # (costs.loc['global_operational_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].
-  #  to_csv(output_path / f'{building.name}_op.csv'))
-  # costs.loc['global_maintenance_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].to_csv(
-  #   output_path / f'{building.name}_m.csv')
-  print(building.name)
-  investment_cost = costs.loc['global_capital_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].loc[0, 'D3020_heat_and_cooling_generating_systems']
+  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}']
-  print(investment_cost)
-  print(lcc_capex)
+  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')

hub/city_model_structure/building.py

@@ -484,7 +484,7 @@ class Building(CityObject):
       monthly_values = PeakLoads().peak_loads_from_hourly(self.domestic_hot_water_heat_demand[cte.HOUR])
     if monthly_values is None:
       return None
-    results[cte.MONTH] = [x for x in monthly_values]
+    results[cte.MONTH] = [x / cte.WATTS_HOUR_TO_JULES for x in monthly_values]
     results[cte.YEAR] = [max(monthly_values) / cte.WATTS_HOUR_TO_JULES]
     return results
 

input_files/processed_output -single_building.geojson

@@ -0,0 +1,50 @@
+{
+    "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

@@ -1,78 +1,52 @@
 import pandas as pd
-from scripts.geojson_creator import process_geojson
 from pathlib import Path
-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 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 SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT_AND_PV
-import hub.helpers.constants as cte
-from hub.exports.exports_factory import ExportsFactory
-from scripts.solar_angles import CitySolarAngles
-from scripts.pv_sizing_and_simulation import PVSizingSimulation
+from scripts.costs.constants import SYSTEM_RETROFIT_AND_PV
 # Specify the GeoJSON file path
-location = [45.49034212153445, -73.61435648647083]
-geojson_file = process_geojson(x=location[1], y=location[0], diff=0.0005)
-file_path = (Path(__file__).parent / 'input_files' / 'output_buildings.geojson')
-# Specify the output path for the PDF file
+file_path = (Path(__file__).parent / 'input_files' / 'processed_output -single_building.geojson')
 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()
-solar_angles = CitySolarAngles(city.name,
-                               city.latitude,
-                               city.longitude,
-                               tilt_angle=45,
-                               surface_azimuth_angle=180).calculate
 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()
-  # 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)
 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.to_csv(output_path / f'{building.name}_lcc.csv')
   costs.loc['global_capital_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].to_csv(
     output_path / f'{building.name}_cc.csv')
-  # (costs.loc['global_operational_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].
-  #  to_csv(output_path / f'{building.name}_op.csv'))
-  # costs.loc['global_maintenance_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].to_csv(
-  #   output_path / f'{building.name}_m.csv')
-  print(building.name)
-  investment_cost = costs.loc['global_capital_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].loc[0, 'D3020_heat_and_cooling_generating_systems']
+  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}']
-  print(investment_cost)
-  print(lcc_capex)
+  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
 
-print(sum_floor_area)
+df.to_csv(output_path / 'economic analysis.csv')

scripts/costs/capital_costs.py

@@ -155,13 +155,12 @@ class CapitalCosts(CostBase):
     capital_cost_energy_storage_equipment = 0
     capital_cost_distribution_equipment = 0
     capital_cost_lighting = 0
-    capital_cost_pv = self._surface_pv * chapter.item('D2010_photovoltaic_system').initial_investment[0]
+    capital_cost_pv = 0
     for (i, component) in enumerate(system_components):
       if component_categories[i] == 'generation':
         capital_cost_heating_and_cooling_equipment += chapter.item(component).initial_investment[0] * component_sizes[i]
       elif component_categories[i] == 'dhw':
-        capital_cost_domestic_hot_water_equipment += chapter.item(component).initial_investment[0] * \
-                                                      component_sizes[i]
+        capital_cost_domestic_hot_water_equipment += 0
       elif component_categories[i] == 'distribution':
         capital_cost_distribution_equipment += chapter.item(component).initial_investment[0] * \
                                                       component_sizes[i]
@@ -237,7 +236,7 @@ class CapitalCosts(CostBase):
               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
             elif component_categories[i] == 'dhw':
-              reposition_cost_domestic_hot_water_equipment = chapter.item(component).reposition[0] * component_sizes[i] * costs_increase
+              reposition_cost_domestic_hot_water_equipment = 0
               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
@@ -248,13 +247,13 @@ class CapitalCosts(CostBase):
       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 * chapter.item('D2010_photovoltaic_system').reposition[0] * costs_increase
+              self._surface_pv * 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 * chapter.item('D2010_photovoltaic_system').reposition[0] * costs_increase
+              self._surface_pv * 0 * costs_increase
           )
 
   def system_components(self):

scripts/system_simulation_models/archetype13.py

@@ -45,6 +45,8 @@ class Archetype13:
     dhw_tes = dhw_hp.energy_storage_systems[0]
     dhw_tes.volume = round(
       (self._domestic_hot_water_peak_load * storage_factor * 3600) / (cte.WATER_HEAT_CAPACITY * cte.WATER_DENSITY * 10))
+    if float(dhw_tes.volume) == 0:
+      dhw_tes.volume = 1
     return dhw_hp, dhw_tes
 
   def heating_system_simulation(self):