Merge pull request 'fix: infiltration_rate_area values for the new cerc archetypes are added' (#3) from infiltration_cerc_catalogues into main

Reviewed-on: #3

building_modelling/ep_workflow.py

@@ -1,45 +0,0 @@
-import glob
-import os
-import sys
-from pathlib import Path
-import csv
-from hub.exports.energy_building_exports_factory import EnergyBuildingsExportsFactory
-from hub.imports.results_factory import ResultFactory
-
-sys.path.append('../energy_system_modelling_package/')
-
-
-def energy_plus_workflow(city, output_path):
-  try:
-    # city = city
-    out_path = output_path
-    files = glob.glob(f'{out_path}/*')
-
-    # for file in files:
-    #   if file != '.gitignore':
-    #     os.remove(file)
-    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()

building_modelling/geojson_creator.py

@@ -1,37 +0,0 @@
-import json
-from shapely import Polygon
-from shapely import Point
-from pathlib import Path
-
-
-def process_geojson(x, y, diff, 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()
-  if not expansion:
-    output_file = Path('./input_files/output_buildings.geojson').resolve()
-  else:
-    output_file = Path('./input_files/output_buildings_expanded.geojson').resolve()
-  buildings_in_region = []
-
-  with open(geojson_file, 'r') as file:
-    city = json.load(file)
-    buildings = city['features']
-
-  for building in buildings:
-    coordinates = building['geometry']['coordinates'][0]
-    building_polygon = Polygon(coordinates)
-    centroid = Point(building_polygon.centroid)
-
-    if centroid.within(selection_box):
-      buildings_in_region.append(building)
-
-  output_region = {"type": "FeatureCollection",
-                   "features": buildings_in_region}
-
-  with open(output_file, 'w') as file:
-    file.write(json.dumps(output_region, indent=2))
-
-  return output_file

costing_package/capital_costs.py

@@ -1,417 +0,0 @@
-"""
-Capital costs module
-SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
-Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
-Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
-"""
-import math
-
-import pandas as pd
-import numpy_financial as npf
-from hub.city_model_structure.building import Building
-import hub.helpers.constants as cte
-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):
-  """
-  Capital costs class
-  """
-
-  def __init__(self, building: Building, configuration: Configuration):
-    super().__init__(building, configuration)
-    self._yearly_capital_costs = pd.DataFrame(
-      index=self._rng,
-      columns=[
-        'B2010_opaque_walls',
-        'B2020_transparent',
-        'B3010_opaque_roof',
-        'B1010_superstructure',
-        'D2010_photovoltaic_system',
-        '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'
-    )
-    self._yearly_capital_costs.loc[0, 'B2010_opaque_walls'] = 0
-    self._yearly_capital_costs.loc[0, 'B2020_transparent'] = 0
-    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_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(
-      index=self._rng,
-      columns=[
-        'Subsidies construction',
-        'Subsidies HVAC',
-        'Subsidies PV'
-      ],
-      dtype='float'
-    )
-    self._yearly_capital_incomes.loc[0, 'Subsidies construction'] = 0
-    self._yearly_capital_incomes.loc[0, 'Subsidies HVAC'] = 0
-    self._yearly_capital_incomes.loc[0, 'Subsidies PV'] = 0
-    self._yearly_capital_costs.fillna(0, inplace=True)
-    self._own_capital = 1 - self._configuration.percentage_credit
-    self._surface_pv = 0
-    for roof in self._building.roofs:
-      self._surface_pv += roof.solid_polygon.area * roof.solar_collectors_area_reduction_factor
-
-    for roof in self._building.roofs:
-      if roof.installed_solar_collector_area is not None:
-        self._surface_pv += roof.installed_solar_collector_area
-      else:
-        self._surface_pv += roof.solid_polygon.area * roof.solar_collectors_area_reduction_factor
-  def calculate(self) -> tuple[pd.DataFrame, pd.DataFrame]:
-    self.skin_capital_cost()
-    self.energy_system_capital_cost()
-    self.skin_yearly_capital_costs()
-    self.yearly_energy_system_costs()
-    self.yearly_incomes()
-    return self._yearly_capital_costs, self._yearly_capital_incomes
-
-  def skin_capital_cost(self):
-    """
-    calculating skin costs
-    :return:
-    """
-    surface_opaque = 0
-    surface_transparent = 0
-    surface_roof = 0
-    surface_ground = 0
-
-    for thermal_zone in self._building.thermal_zones_from_internal_zones:
-      for thermal_boundary in thermal_zone.thermal_boundaries:
-        if thermal_boundary.type == 'Ground':
-          surface_ground += thermal_boundary.opaque_area
-        elif thermal_boundary.type == 'Roof':
-          surface_roof += thermal_boundary.opaque_area
-        elif thermal_boundary.type == 'Wall':
-          surface_opaque += thermal_boundary.opaque_area * (1 - thermal_boundary.window_ratio)
-          surface_transparent += thermal_boundary.opaque_area * thermal_boundary.window_ratio
-
-    chapter = self._capital_costs_chapter.chapter('B_shell')
-    capital_cost_opaque = surface_opaque * chapter.item('B2010_opaque_walls').refurbishment[0]
-    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, 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
-      self._yearly_capital_costs.loc[0, 'B1010_superstructure'] = capital_cost_ground * self._own_capital
-    capital_cost_skin = capital_cost_opaque + capital_cost_ground + capital_cost_transparent + capital_cost_roof
-    return capital_cost_opaque, capital_cost_transparent, capital_cost_roof, capital_cost_ground, capital_cost_skin
-
-  def skin_yearly_capital_costs(self):
-    skin_capital_cost = self.skin_capital_cost()
-    for year in range(1, self._configuration.number_of_years):
-      self._yearly_capital_costs.loc[year, 'B2010_opaque_walls'] = (
-        -npf.pmt(
-          self._configuration.interest_rate,
-          self._configuration.credit_years,
-          skin_capital_cost[0] * self._configuration.percentage_credit
-        )
-      )
-      self._yearly_capital_costs.loc[year, 'B2020_transparent'] = (
-        -npf.pmt(
-          self._configuration.interest_rate,
-          self._configuration.credit_years,
-          skin_capital_cost[1] * self._configuration.percentage_credit
-        )
-      )
-      self._yearly_capital_costs.loc[year, 'B3010_opaque_roof'] = (
-        -npf.pmt(
-          self._configuration.interest_rate,
-          self._configuration.credit_years,
-          skin_capital_cost[2] * self._configuration.percentage_credit
-        )
-      )
-      self._yearly_capital_costs.loc[year, 'B1010_superstructure'] = (
-        -npf.pmt(
-          self._configuration.interest_rate,
-          self._configuration.credit_years,
-          skin_capital_cost[3] * self._configuration.percentage_credit
-        )
-      )
-
-  def energy_system_capital_cost(self):
-    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 = self._surface_pv * chapter.item('D2010_photovoltaic_system').initial_investment[0]
-    for (i, component) in enumerate(system_components):
-      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 += 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]
-      else:
-        capital_cost_energy_storage_equipment += chapter.item(component).initial_investment[0] * component_sizes[i]
-
-    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, 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, '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, '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_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')
-    system_investment_costs = self.energy_system_capital_cost()
-    system_components, component_categories, component_sizes = self.system_components()
-    pv = False
-    for energy_system in self._building.energy_systems:
-      for generation_system in energy_system.generation_systems:
-        if generation_system.system_type == cte.PHOTOVOLTAIC:
-          pv = True
-    for year in range(1, self._configuration.number_of_years):
-      costs_increase = math.pow(1 + self._configuration.consumer_price_index, year)
-      self._yearly_capital_costs.loc[year, 'D2010_photovoltaic_system'] = (
-        -npf.pmt(
-          self._configuration.interest_rate,
-          self._configuration.credit_years,
-          system_investment_costs[0] * self._configuration.percentage_credit
-        )
-      )
-      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, '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, '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, '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] == '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 = (
-                  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, '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, '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 * 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 * chapter.item('D2010_photovoltaic_system').reposition[0] * costs_increase
-          )
-
-  def system_components(self):
-    system_components = []
-    component_categories = []
-    sizes = []
-    energy_systems = self._building.energy_systems
-    for energy_system in energy_systems:
-      demand_types = energy_system.demand_types
-      generation_systems = energy_system.generation_systems
-      distribution_systems = energy_system.distribution_systems
-      for generation_system in generation_systems:
-        if generation_system.system_type != cte.PHOTOVOLTAIC:
-          heating_capacity = generation_system.nominal_heat_output or 0
-          cooling_capacity = generation_system.nominal_cooling_output or 0
-          installed_capacity = max(heating_capacity, cooling_capacity) / 1000
-          if cte.DOMESTIC_HOT_WATER in demand_types and cte.HEATING not in demand_types:
-            component_categories.append('dhw')
-            sizes.append(installed_capacity)
-            if generation_system.system_type == cte.HEAT_PUMP:
-              system_components.append(self.heat_pump_type(generation_system, domestic_how_water=True))
-            elif generation_system.system_type == cte.BOILER and generation_system.fuel_type == cte.ELECTRICITY:
-              system_components.append(self.boiler_type(generation_system))
-            else:
-              system_components.append('D302010_template_heat')
-          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)
-              system_components.append(item_type)
-            elif generation_system.system_type == cte.BOILER:
-              item_type = self.boiler_type(generation_system)
-              system_components.append(item_type)
-            else:
-              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:
-              if storage_system.type_energy_stored == 'thermal':
-                component_categories.append('thermal storage')
-                sizes.append(storage_system.volume or 0)
-                system_components.append('D306010_storage_tank')
-      if distribution_systems is not None:
-        for distribution_system in distribution_systems:
-          component_categories.append('distribution')
-          sizes.append(self._building.cooling_peak_load[cte.YEAR][0] / 1000)
-          system_components.append('D3040_distribution_systems')
-    return system_components, component_categories, sizes
-
-  def yearly_incomes(self):
-    capital_cost_skin = self.skin_capital_cost()[-1]
-    system_investment_cost = self.energy_system_capital_cost()
-    capital_cost_hvac = system_investment_cost[-1]
-    capital_cost_pv = system_investment_cost[0]
-
-    self._yearly_capital_incomes.loc[0, 'Subsidies construction'] = (
-        capital_cost_skin * self._archetype.income.construction_subsidy/100
-    )
-    self._yearly_capital_incomes.loc[0, 'Subsidies HVAC'] = capital_cost_hvac * self._archetype.income.hvac_subsidy/100
-    self._yearly_capital_incomes.loc[0, 'Subsidies PV'] = capital_cost_pv * self._archetype.income.photovoltaic_subsidy/100
-    self._yearly_capital_incomes.fillna(0, inplace=True)
-
-  @staticmethod
-  def heat_pump_type(generation_system, domestic_how_water=False):
-    source_medium = generation_system.source_medium
-    supply_medium = generation_system.supply_medium
-    if domestic_how_water:
-      heat_pump_item = 'D4010_hot_water_heat_pump'
-    else:
-      if source_medium == cte.AIR and supply_medium == cte.WATER:
-        heat_pump_item = 'D302020_air_to_water_heat_pump'
-      elif source_medium == cte.AIR and supply_medium == cte.AIR:
-        heat_pump_item = 'D302050_air_to_air_heat_pump'
-      elif source_medium == cte.GROUND and supply_medium == cte.WATER:
-        heat_pump_item = 'D302030_ground_to_water_heat_pump'
-      elif source_medium == cte.GROUND and supply_medium == cte.AIR:
-        heat_pump_item = 'D302100_ground_to_air_heat_pump'
-      elif source_medium == cte.WATER and supply_medium == cte.WATER:
-        heat_pump_item = 'D302040_water_to_water_heat_pump'
-      elif source_medium == cte.WATER and supply_medium == cte.AIR:
-        heat_pump_item = 'D302110_water_to_air_heat_pump'
-      else:
-        heat_pump_item = 'D302010_template_heat'
-    return heat_pump_item
-
-  @staticmethod
-  def boiler_type(generation_system):
-    fuel = generation_system.fuel_type
-    if fuel == cte.ELECTRICITY:
-      boiler_item = 'D302080_electrical_boiler'
-    elif fuel == cte.GAS:
-      boiler_item = 'D302070_natural_gas_boiler'
-    else:
-      boiler_item = 'D302010_template_heat'
-    return boiler_item
-
-
-
-

costing_package/configuration.py

@@ -1,238 +0,0 @@
-"""
-Configuration module
-SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
-Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
-Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
-"""
-from hub.catalog_factories.costs_catalog_factory import CostsCatalogFactory
-from hub.catalog_factories.catalog import Catalog
-
-
-class Configuration:
-  """
-  Configuration class
-  """
-
-  def __init__(self,
-               number_of_years,
-               percentage_credit,
-               interest_rate,
-               credit_years,
-               consumer_price_index,
-               electricity_peak_index,
-               electricity_price_index,
-               gas_price_index,
-               discount_rate,
-               retrofitting_year_construction,
-               factories_handler,
-               retrofit_scenario,
-               fuel_type,
-               dictionary,
-               fuel_tariffs
-               ):
-    self._number_of_years = number_of_years
-    self._percentage_credit = percentage_credit
-    self._interest_rate = interest_rate
-    self._credit_years = credit_years
-    self._consumer_price_index = consumer_price_index
-    self._electricity_peak_index = electricity_peak_index
-    self._electricity_price_index = electricity_price_index
-    self._gas_price_index = gas_price_index
-    self._discount_rate = discount_rate
-    self._retrofitting_year_construction = retrofitting_year_construction
-    self._factories_handler = factories_handler
-    self._costs_catalog = CostsCatalogFactory(factories_handler).catalog
-    self._retrofit_scenario = retrofit_scenario
-    self._fuel_type = fuel_type
-    self._dictionary = dictionary
-    self._fuel_tariffs = fuel_tariffs
-
-  @property
-  def number_of_years(self):
-    """
-    Get number of years
-    """
-    return self._number_of_years
-
-  @number_of_years.setter
-  def number_of_years(self, value):
-    """
-    Set number of years
-    """
-    self._number_of_years = value
-
-  @property
-  def percentage_credit(self):
-    """
-    Get percentage credit
-    """
-    return self._percentage_credit
-
-  @percentage_credit.setter
-  def percentage_credit(self, value):
-    """
-    Set percentage credit
-    """
-    self._percentage_credit = value
-
-  @property
-  def interest_rate(self):
-    """
-    Get interest rate
-    """
-    return self._interest_rate
-
-  @interest_rate.setter
-  def interest_rate(self, value):
-    """
-    Set interest rate
-    """
-    self._interest_rate = value
-
-  @property
-  def credit_years(self):
-    """
-    Get credit years
-    """
-    return self._credit_years
-
-  @credit_years.setter
-  def credit_years(self, value):
-    """
-    Set credit years
-    """
-    self._credit_years = value
-
-  @property
-  def consumer_price_index(self):
-    """
-    Get consumer price index
-    """
-    return self._consumer_price_index
-
-  @consumer_price_index.setter
-  def consumer_price_index(self, value):
-    """
-    Set consumer price index
-    """
-    self._consumer_price_index = value
-
-  @property
-  def electricity_peak_index(self):
-    """
-    Get electricity peak index
-    """
-    return self._electricity_peak_index
-
-  @electricity_peak_index.setter
-  def electricity_peak_index(self, value):
-    """
-    Set electricity peak index
-    """
-    self._electricity_peak_index = value
-
-  @property
-  def electricity_price_index(self):
-    """
-    Get electricity price index
-    """
-    return self._electricity_price_index
-
-  @electricity_price_index.setter
-  def electricity_price_index(self, value):
-    """
-    Set electricity price index
-    """
-    self._electricity_price_index = value
-
-  @property
-  def gas_price_index(self):
-    """
-    Get gas price index
-    """
-    return self._gas_price_index
-
-  @gas_price_index.setter
-  def gas_price_index(self, value):
-    """
-    Set gas price index
-    """
-    self._gas_price_index = value
-
-  @property
-  def discount_rate(self):
-    """
-    Get discount rate
-    """
-    return self._discount_rate
-
-  @discount_rate.setter
-  def discount_rate(self, value):
-    """
-    Set discount rate
-    """
-    self._discount_rate = value
-
-  @property
-  def retrofitting_year_construction(self):
-    """
-    Get retrofitting year construction
-    """
-    return self._retrofitting_year_construction
-
-  @retrofitting_year_construction.setter
-  def retrofitting_year_construction(self, value):
-    """
-    Set retrofitting year construction
-    """
-    self._retrofitting_year_construction = value
-
-  @property
-  def factories_handler(self):
-    """
-    Get factories handler
-    """
-    return self._factories_handler
-
-  @factories_handler.setter
-  def factories_handler(self, value):
-    """
-    Set factories handler
-    """
-    self._factories_handler = value
-
-  @property
-  def costs_catalog(self) -> Catalog:
-    """
-    Get costs catalog
-    """
-    return self._costs_catalog
-
-  @property
-  def retrofit_scenario(self):
-    """
-    Get retrofit scenario
-    """
-    return self._retrofit_scenario
-
-  @property
-  def fuel_type(self):
-    """
-    Get fuel type (0: Electricity, 1: Gas)
-    """
-    return self._fuel_type
-
-  @property
-  def dictionary(self):
-    """
-    Get hub function to cost function dictionary
-    """
-    return self._dictionary
-
-  @property
-  def fuel_tariffs(self):
-    """
-    Get fuel tariffs
-    """
-    return self._fuel_tariffs

costing_package/constants.py

@@ -1,23 +0,0 @@
-"""
-Constants module
-SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
-Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
-Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
-"""
-
-# constants
-CURRENT_STATUS = 0
-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,
-  SYSTEM_RETROFIT
-]

costing_package/cost.py

@@ -1,170 +0,0 @@
-"""
-Cost module
-SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
-Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
-Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
-"""
-
-import pandas as pd
-import numpy_financial as npf
-from hub.city_model_structure.building import Building
-from hub.helpers.dictionaries import Dictionaries
-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
-
-
-class Cost:
-  """
-  Cost class
-  """
-
-  def __init__(self,
-               building: Building,
-               number_of_years=31,
-               percentage_credit=0,
-               interest_rate=0.04,
-               credit_years=15,
-               consumer_price_index=0.04,
-               electricity_peak_index=0.05,
-               electricity_price_index=0.05,
-               fuel_price_index=0.05,
-               discount_rate=0.03,
-               retrofitting_year_construction=2020,
-               factories_handler='montreal_new',
-               retrofit_scenario=CURRENT_STATUS,
-               dictionary=None,
-               fuel_tariffs=None):
-    if fuel_tariffs is None:
-      fuel_tariffs = ['Electricity-D', 'Gas-Energir']
-    if dictionary is None:
-      dictionary = Dictionaries().hub_function_to_montreal_custom_costs_function
-    self._building = building
-    fuel_type = self._building.energy_consumption_breakdown.keys()
-    self._configuration = Configuration(number_of_years,
-                                        percentage_credit,
-                                        interest_rate, credit_years,
-                                        consumer_price_index,
-                                        electricity_peak_index,
-                                        electricity_price_index,
-                                        fuel_price_index,
-                                        discount_rate,
-                                        retrofitting_year_construction,
-                                        factories_handler,
-                                        retrofit_scenario,
-                                        fuel_type,
-                                        dictionary,
-                                        fuel_tariffs)
-
-  @property
-  def building(self) -> Building:
-    """
-    Get current building.
-    """
-    return self._building
-
-  def _npv_from_list(self, list_cashflow):
-    return npf.npv(self._configuration.discount_rate, list_cashflow)
-
-  @property
-  def life_cycle(self) -> pd.DataFrame:
-    """
-    Get complete life cycle costs
-    :return: DataFrame
-    """
-    results = pd.DataFrame()
-    global_capital_costs, global_capital_incomes = CapitalCosts(self._building, self._configuration).calculate()
-    global_end_of_life_costs = EndOfLifeCosts(self._building, self._configuration).calculate()
-    global_operational_costs = TotalOperationalCosts(self._building, self._configuration).calculate()
-    global_maintenance_costs = TotalMaintenanceCosts(self._building, self._configuration).calculate()
-    global_operational_incomes = TotalOperationalIncomes(self._building, self._configuration).calculate()
-
-    df_capital_costs_skin = (
-        global_capital_costs['B2010_opaque_walls'] +
-        global_capital_costs['B2020_transparent'] +
-        global_capital_costs['B3010_opaque_roof'] +
-        global_capital_costs['B1010_superstructure']
-    )
-    df_capital_costs_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']
-    )
-
-    df_end_of_life_costs = global_end_of_life_costs['End_of_life_costs']
-    operational_costs_list = [
-      global_operational_costs['Fixed Costs Electricity Peak'],
-      global_operational_costs['Fixed Costs Electricity Monthly'],
-      global_operational_costs['Variable Costs Electricity']
-      ]
-    additional_costs = [
-                         global_operational_costs[f'Fixed Costs {fuel}'] for fuel in
-                         self._building.energy_consumption_breakdown.keys() if fuel != cte.ELECTRICITY
-                       ] + [
-                         global_operational_costs[f'Variable Costs {fuel}'] for fuel in
-                         self._building.energy_consumption_breakdown.keys() if fuel != cte.ELECTRICITY
-                       ]
-    df_operational_costs = sum(operational_costs_list + additional_costs)
-    df_maintenance_costs = (
-        global_maintenance_costs['Heating_maintenance'] +
-        global_maintenance_costs['Cooling_maintenance'] +
-        global_maintenance_costs['PV_maintenance']
-    )
-    df_operational_incomes = global_operational_incomes['Incomes electricity']
-    df_capital_incomes = (
-        global_capital_incomes['Subsidies construction'] +
-        global_capital_incomes['Subsidies HVAC'] +
-        global_capital_incomes['Subsidies PV']
-    )
-
-    life_cycle_costs_capital_skin = self._npv_from_list(df_capital_costs_skin.values.tolist())
-    life_cycle_costs_capital_systems = self._npv_from_list(df_capital_costs_systems.values.tolist())
-    life_cycle_costs_end_of_life_costs = self._npv_from_list(df_end_of_life_costs.values.tolist())
-    life_cycle_operational_costs = self._npv_from_list(df_operational_costs)
-    life_cycle_maintenance_costs = self._npv_from_list(df_maintenance_costs.values.tolist())
-    life_cycle_operational_incomes = self._npv_from_list(df_operational_incomes.values.tolist())
-    life_cycle_capital_incomes = self._npv_from_list(df_capital_incomes.values.tolist())
-
-    results[f'Scenario {self._configuration.retrofit_scenario}'] = [
-      life_cycle_costs_capital_skin,
-      life_cycle_costs_capital_systems,
-      life_cycle_costs_end_of_life_costs,
-      life_cycle_operational_costs,
-      life_cycle_maintenance_costs,
-      life_cycle_operational_incomes,
-      life_cycle_capital_incomes,
-      global_capital_costs,
-      global_capital_incomes,
-      global_end_of_life_costs,
-      global_operational_costs,
-      global_maintenance_costs,
-      global_operational_incomes
-    ]
-
-    results.index = [
-      'total_capital_costs_skin',
-      'total_capital_costs_systems',
-      'end_of_life_costs',
-      'total_operational_costs',
-      'total_maintenance_costs',
-      'operational_incomes',
-      'capital_incomes',
-      'global_capital_costs',
-      'global_capital_incomes',
-      'global_end_of_life_costs',
-      'global_operational_costs',
-      'global_maintenance_costs',
-      'global_operational_incomes'
-    ]
-    return results

costing_package/cost_base.py

@@ -1,40 +0,0 @@
-"""
-Cost base module
-SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
-Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
-Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
-"""
-
-from hub.city_model_structure.building import Building
-
-from costing_package.configuration import Configuration
-
-
-class CostBase:
-  """
-  Abstract base class for the costs
-  """
-  def __init__(self, building: Building, configuration: Configuration):
-    self._building = building
-    self._configuration = configuration
-    self._total_floor_area = 0
-    for thermal_zone in building.thermal_zones_from_internal_zones:
-      self._total_floor_area += thermal_zone.total_floor_area
-    self._archetype = None
-    self._capital_costs_chapter = None
-    for archetype in self._configuration.costs_catalog.entries().archetypes:
-      if configuration.dictionary[str(building.function)] == str(archetype.function):
-        self._archetype = archetype
-        self._capital_costs_chapter = self._archetype.capital_cost
-        break
-    if not self._archetype:
-      raise KeyError(f'archetype not found for function {building.function}')
-
-    self._rng = range(configuration.number_of_years)
-
-  def calculate(self):
-    """
-    Raises not implemented exception
-    """
-    raise NotImplementedError()

costing_package/end_of_life_costs.py

@@ -1,38 +0,0 @@
-"""
-End of life costs module
-SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
-Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
-Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
-"""
-import math
-import pandas as pd
-from hub.city_model_structure.building import Building
-
-from costing_package.configuration import Configuration
-from costing_package.cost_base import CostBase
-
-
-class EndOfLifeCosts(CostBase):
-  """
-  End of life costs class
-  """
-  def __init__(self, building: Building, configuration: Configuration):
-    super().__init__(building, configuration)
-    self._yearly_end_of_life_costs = pd.DataFrame(index=self._rng, columns=['End_of_life_costs'], dtype='float')
-
-  def calculate(self):
-    """
-    Calculate end of life costs
-    :return: pd.DataFrame
-    """
-    archetype = self._archetype
-    total_floor_area = self._total_floor_area
-    for year in range(1, self._configuration.number_of_years + 1):
-      price_increase = math.pow(1 + self._configuration.consumer_price_index, year)
-      if year == self._configuration.number_of_years:
-        self._yearly_end_of_life_costs.at[year, 'End_of_life_costs'] = (
-            total_floor_area * archetype.end_of_life_cost * price_increase
-        )
-    self._yearly_end_of_life_costs.fillna(0, inplace=True)
-    return self._yearly_end_of_life_costs

costing_package/peak_load.py

@@ -1,56 +0,0 @@
-"""
-Peak load module
-SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
-Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
-Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
-"""
-
-import pandas as pd
-
-import hub.helpers.constants as cte
-
-
-class PeakLoad:
-  """
-  Peak load class
-  """
-
-  def __init__(self, building):
-    self._building = building
-
-  @property
-  def electricity_peak_load(self):
-    """
-    Get the electricity peak load in W
-    """
-    array = [None] * 12
-    heating = 0
-    cooling = 0
-    for system in self._building.energy_systems:
-      if cte.HEATING in system.demand_types:
-        heating = 1
-      if cte.COOLING in system.demand_types:
-        cooling = 1
-    if cte.MONTH in self._building.heating_peak_load.keys() and cte.MONTH in self._building.cooling_peak_load.keys():
-      peak_lighting = self._building.lighting_peak_load[cte.YEAR][0]
-      peak_appliances = self._building.appliances_peak_load[cte.YEAR][0]
-      monthly_electricity_peak = [0.9 * peak_lighting + 0.7 * peak_appliances] * 12
-      conditioning_peak = max(self._building.heating_peak_load[cte.MONTH], self._building.cooling_peak_load[cte.MONTH])
-      for i in range(len(conditioning_peak)):
-        if cooling == 1 and heating == 1:
-          conditioning_peak[i] = conditioning_peak[i]
-          continue
-        elif cooling == 0:
-          conditioning_peak[i] = self._building.heating_peak_load[cte.MONTH][i] * heating
-        else:
-          conditioning_peak[i] = self._building.cooling_peak_load[cte.MONTH][i] * cooling
-        monthly_electricity_peak[i] += 0.8 * conditioning_peak[i]
-
-      electricity_peak_load_results = pd.DataFrame(
-        monthly_electricity_peak,
-        columns=[f'electricity peak load W']
-      )
-    else:
-      electricity_peak_load_results = pd.DataFrame(array, columns=[f'electricity peak load W'])
-    return electricity_peak_load_results

costing_package/total_maintenance_costs.py

@@ -1,138 +0,0 @@
-"""
-Total maintenance costs module
-SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
-Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
-Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
-"""
-import math
-import pandas as pd
-from hub.city_model_structure.building import Building
-import hub.helpers.constants as cte
-
-from costing_package.configuration import Configuration
-from costing_package.cost_base import CostBase
-
-
-class TotalMaintenanceCosts(CostBase):
-  """
-  Total maintenance costs class
-  """
-  def __init__(self, building: Building, configuration: Configuration):
-    super().__init__(building, configuration)
-    self._yearly_maintenance_costs = pd.DataFrame(
-      index=self._rng,
-      columns=[
-        'Heating_maintenance',
-        'Cooling_maintenance',
-        'DHW_maintenance',
-        'PV_maintenance'
-      ],
-      dtype='float'
-    )
-
-  def calculate(self) -> pd.DataFrame:
-    """
-    Calculate total maintenance costs
-    :return: pd.DataFrame
-    """
-    building = self._building
-    archetype = self._archetype
-    # todo: change area pv when the variable exists
-    roof_area = 0
-    surface_pv = 0
-    for roof in self._building.roofs:
-      if roof.installed_solar_collector_area is not None:
-        surface_pv += roof.installed_solar_collector_area
-      else:
-        surface_pv = roof_area * 0.5
-
-    energy_systems = building.energy_systems
-    maintenance_heating_0 = 0
-    maintenance_cooling_0 = 0
-    maintenance_dhw_0 = 0
-    heating_equipments = {}
-    cooling_equipments = {}
-    dhw_equipments = {}
-    for energy_system in energy_systems:
-      if cte.COOLING in energy_system.demand_types:
-        for generation_system in energy_system.generation_systems:
-          if generation_system.fuel_type == cte.ELECTRICITY:
-            if generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.AIR:
-              cooling_equipments['air_source_heat_pump'] = generation_system.nominal_cooling_output / 1000
-            elif generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.GROUND:
-              cooling_equipments['ground_source_heat_pump'] = generation_system.nominal_cooling_output / 1000
-            elif generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.WATER:
-              cooling_equipments['water_source_heat_pump'] = generation_system.nominal_cooling_output / 1000
-            else:
-              cooling_equipments['general_cooling_equipment'] = generation_system.nominal_cooling_output / 1000
-      if cte.HEATING in energy_system.demand_types:
-        for generation_system in energy_system.generation_systems:
-          if generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.AIR:
-            heating_equipments['air_source_heat_pump'] = generation_system.nominal_heat_output / 1000
-          elif generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.GROUND:
-            heating_equipments['ground_source_heat_pump'] = generation_system.nominal_heat_output / 1000
-          elif generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.WATER:
-            heating_equipments['water_source_heat_pump'] = generation_system.nominal_heat_output / 1000
-          elif generation_system.system_type == cte.BOILER and generation_system.fuel_type == cte.GAS:
-            heating_equipments['gas_boiler'] = generation_system.nominal_heat_output / 1000
-          elif generation_system.system_type == cte.BOILER and generation_system.fuel_type == cte.ELECTRICITY:
-            heating_equipments['electric_boiler'] = generation_system.nominal_heat_output / 1000
-          else:
-            heating_equipments['general_heating_equipment'] = generation_system.nominal_heat_output / 1000
-      if cte.DOMESTIC_HOT_WATER in energy_system.demand_types and cte.HEATING not in energy_system.demand_types:
-        for generation_system in energy_system.generation_systems:
-          if generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.AIR:
-            dhw_equipments['air_source_heat_pump'] = generation_system.nominal_heat_output / 1000
-          elif generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.GROUND:
-            dhw_equipments['ground_source_heat_pump'] = generation_system.nominal_heat_output / 1000
-          elif generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.WATER:
-            dhw_equipments['water_source_heat_pump'] = generation_system.nominal_heat_output / 1000
-          elif generation_system.system_type == cte.BOILER and generation_system.fuel_type == cte.GAS:
-            dhw_equipments['gas_boiler'] = generation_system.nominal_heat_output / 1000
-          elif generation_system.system_type == cte.BOILER and generation_system.fuel_type == cte.ELECTRICITY:
-            dhw_equipments['electric_boiler'] = generation_system.nominal_heat_output / 1000
-          else:
-            dhw_equipments['general_heating_equipment'] = generation_system.nominal_heat_output / 1000
-
-
-    for heating_equipment in heating_equipments:
-      component = self.search_hvac_equipment(heating_equipment)
-      maintenance_cost = component.maintenance[0]
-      maintenance_heating_0 += (heating_equipments[heating_equipment] * maintenance_cost)
-
-    for cooling_equipment in cooling_equipments:
-      component = self.search_hvac_equipment(cooling_equipment)
-      maintenance_cost = component.maintenance[0]
-      maintenance_cooling_0 += (cooling_equipments[cooling_equipment] * maintenance_cost)
-
-    for dhw_equipment in dhw_equipments:
-      component = self.search_hvac_equipment(dhw_equipment)
-      maintenance_cost = component.maintenance[0]
-      maintenance_dhw_0 += (dhw_equipments[dhw_equipment] * maintenance_cost)
-
-    maintenance_pv_0 = surface_pv * archetype.operational_cost.maintenance_pv
-
-    for year in range(1, self._configuration.number_of_years + 1):
-      costs_increase = math.pow(1 + self._configuration.consumer_price_index, year)
-      self._yearly_maintenance_costs.loc[year, 'Heating_maintenance'] = (
-          maintenance_heating_0 * costs_increase
-      )
-      self._yearly_maintenance_costs.loc[year, 'Cooling_maintenance'] = (
-          maintenance_cooling_0 * costs_increase
-      )
-      self._yearly_maintenance_costs.loc[year, 'DHW_maintenance'] = (
-          maintenance_dhw_0 * costs_increase
-      )
-      self._yearly_maintenance_costs.loc[year, 'PV_maintenance'] = (
-          maintenance_pv_0 * costs_increase
-      )
-    self._yearly_maintenance_costs.fillna(0, inplace=True)
-    return self._yearly_maintenance_costs
-
-  def search_hvac_equipment(self, equipment_type):
-    for component in self._archetype.operational_cost.maintenance_hvac:
-      if component.type == equipment_type:
-        return component
-
-

costing_package/total_operational_costs.py

@@ -1,237 +0,0 @@
-"""
-Total operational costs module
-SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2024 Project Coder Saeed Ranjbar saeed.ranjbar@mail.concordia.ca
-Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
-"""
-import math
-import pandas as pd
-
-from hub.city_model_structure.building import Building
-import hub.helpers.constants as cte
-
-from costing_package.configuration import Configuration
-from costing_package.cost_base import CostBase
-from costing_package.peak_load import PeakLoad
-
-
-class TotalOperationalCosts(CostBase):
-  """
-  Total Operational costs class
-  """
-
-  def __init__(self, building: Building, configuration: Configuration):
-    super().__init__(building, configuration)
-    columns_list = self.columns()
-    self._yearly_operational_costs = pd.DataFrame(
-      index=self._rng,
-      columns=columns_list,
-      dtype='float'
-    )
-
-  def calculate(self) -> pd.DataFrame:
-    """
-    Calculate total operational costs
-    :return: pd.DataFrame
-    """
-    building = self._building
-    fuel_consumption_breakdown = building.energy_consumption_breakdown
-    archetype = self._archetype
-    total_floor_area = self._total_floor_area
-    if archetype.function == 'residential':
-      factor = total_floor_area / 80
-    else:
-      factor = 1
-    total_electricity_consumption = sum(self._building.energy_consumption_breakdown[cte.ELECTRICITY].values()) / 3600
-    peak_electricity_load = PeakLoad(self._building).electricity_peak_load
-    peak_load_value = peak_electricity_load.max(axis=1)
-    peak_electricity_demand = peak_load_value[1] / 1000  # self._peak_electricity_demand adapted to kW
-    for system_fuel in self._configuration.fuel_type:
-      fuel = None
-      for fuel_tariff in self._configuration.fuel_tariffs:
-        if system_fuel in fuel_tariff:
-          fuel = self.search_fuel(system_fuel, fuel_tariff)
-      if fuel.type == cte.ELECTRICITY:
-        if fuel.variable.rate_type == 'fixed':
-          variable_electricity_cost_year_0 = (
-              total_electricity_consumption * float(fuel.variable.values[0]) / 1000
-          )
-        else:
-          hourly_electricity_consumption = self.hourly_fuel_consumption_profile(fuel.type)
-          hourly_electricity_price_profile = fuel.variable.values * len(hourly_electricity_consumption)
-          hourly_electricity_price = [hourly_electricity_consumption[i] / 1000 * hourly_electricity_price_profile[i]
-                                      for i in range(len(hourly_electricity_consumption))]
-          variable_electricity_cost_year_0 = sum(hourly_electricity_price)
-        peak_electricity_cost_year_0 = peak_electricity_demand * fuel.fixed_power * 12
-        monthly_electricity_cost_year_0 = fuel.fixed_monthly * 12 * factor
-        for year in range(1, self._configuration.number_of_years + 1):
-          price_increase_electricity = math.pow(1 + self._configuration.electricity_price_index, year)
-          price_increase_peak_electricity = math.pow(1 + self._configuration.electricity_peak_index, year)
-          self._yearly_operational_costs.at[year, 'Fixed Costs Electricity Peak'] = (
-              peak_electricity_cost_year_0 * price_increase_peak_electricity
-          )
-          self._yearly_operational_costs.at[year, 'Fixed Costs Electricity Monthly'] = (
-              monthly_electricity_cost_year_0 * price_increase_peak_electricity
-          )
-          if not isinstance(variable_electricity_cost_year_0, pd.DataFrame):
-            variable_costs_electricity = variable_electricity_cost_year_0 * price_increase_electricity
-          else:
-            variable_costs_electricity = float(variable_electricity_cost_year_0.iloc[0] * price_increase_electricity)
-          self._yearly_operational_costs.at[year, 'Variable Costs Electricity'] = (
-            variable_costs_electricity
-          )
-      else:
-        fuel_fixed_cost = fuel.fixed_monthly * 12 * factor
-        if fuel.type == cte.BIOMASS:
-          conversion_factor = 1
-        else:
-          conversion_factor = fuel.density[0]
-        if fuel.variable.rate_type == 'fixed':
-          variable_cost_fuel = (
-              (sum(fuel_consumption_breakdown[fuel.type].values()) / (
-                    1e6 * fuel.lower_heating_value[0] * conversion_factor)) * fuel.variable.values[0])
-
-        else:
-          hourly_fuel_consumption = self.hourly_fuel_consumption_profile(fuel.type)
-          hourly_fuel_price_profile = fuel.variable.values * len(hourly_fuel_consumption)
-          hourly_fuel_price = [hourly_fuel_consumption[i] / (
-                    1e6 * fuel.lower_heating_value[0] * conversion_factor) * hourly_fuel_price_profile[i]
-                                      for i in range(len(hourly_fuel_consumption))]
-          variable_cost_fuel = sum(hourly_fuel_price)
-
-        for year in range(1, self._configuration.number_of_years + 1):
-          price_increase_gas = math.pow(1 + self._configuration.gas_price_index, year)
-          self._yearly_operational_costs.at[year, f'Fixed Costs {fuel.type}'] = fuel_fixed_cost * price_increase_gas
-          self._yearly_operational_costs.at[year, f'Variable Costs {fuel.type}'] = (
-              variable_cost_fuel * price_increase_gas)
-    self._yearly_operational_costs.fillna(0, inplace=True)
-
-    return self._yearly_operational_costs
-
-  def columns(self):
-    columns_list = []
-    fuels = [key for key in self._building.energy_consumption_breakdown.keys()]
-    for fuel in fuels:
-      if fuel == cte.ELECTRICITY:
-        columns_list.append('Fixed Costs Electricity Peak')
-        columns_list.append('Fixed Costs Electricity Monthly')
-        columns_list.append('Variable Costs Electricity')
-      else:
-        columns_list.append(f'Fixed Costs {fuel}')
-        columns_list.append(f'Variable Costs {fuel}')
-
-    return columns_list
-
-  def search_fuel(self, system_fuel, tariff):
-    fuels = self._archetype.operational_cost.fuels
-    for fuel in fuels:
-      if system_fuel == fuel.type and tariff == fuel.variable.name:
-        return fuel
-    raise KeyError(f'fuel  {system_fuel} with {tariff} tariff not found')
-
-
-  def hourly_fuel_consumption_profile(self, fuel_type):
-    hourly_fuel_consumption = []
-    energy_systems = self._building.energy_systems
-    if fuel_type == cte.ELECTRICITY:
-      appliance = self._building.appliances_electrical_demand[cte.HOUR]
-      lighting = self._building.lighting_electrical_demand[cte.HOUR]
-      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(len(self._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_fuel_consumption.append(hourly)
-    else:
-      heating = None
-      dhw = None
-      if cte.HEATING in self._building.energy_consumption_breakdown[fuel_type]:
-        for energy_system in energy_systems:
-          if cte.HEATING in energy_system.demand_types:
-            for generation_system in energy_system.generation_systems:
-              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 cte.DOMESTIC_HOT_WATER in self._building.energy_consumption_breakdown[fuel_type]:
-        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 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]
-
-      for i in range(len(self._building.heating_demand[cte.HOUR])):
-        hourly = 0
-        if heating is not None:
-          hourly += heating[i] / 3600
-        if dhw is not None:
-          hourly += dhw[i] / 3600
-        hourly_fuel_consumption.append(hourly)
-    return hourly_fuel_consumption
-
-
-

costing_package/total_operational_incomes.py

@@ -1,44 +0,0 @@
-"""
-Total operational incomes module
-SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
-Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
-Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
-"""
-import math
-import pandas as pd
-from hub.city_model_structure.building import Building
-import hub.helpers.constants as cte
-
-from costing_package.configuration import Configuration
-from costing_package.cost_base import CostBase
-
-
-class TotalOperationalIncomes(CostBase):
-  """
-  Total operational incomes class
-  """
-  def __init__(self, building: Building, configuration: Configuration):
-    super().__init__(building, configuration)
-    self._yearly_operational_incomes = pd.DataFrame(index=self._rng, columns=['Incomes electricity'], dtype='float')
-
-  def calculate(self) -> pd.DataFrame:
-    """
-    Calculate total operational incomes
-    :return: pd.DataFrame
-    """
-    building = self._building
-    archetype = self._archetype
-    if cte.YEAR not in building.onsite_electrical_production:
-      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)
-      price_export = archetype.income.electricity_export  # to account for unit change
-      self._yearly_operational_incomes.loc[year, 'Incomes electricity'] = (
-        (onsite_electricity_production / 3.6e6) * price_export * price_increase_electricity
-      )
-
-    self._yearly_operational_incomes.fillna(0, inplace=True)
-    return self._yearly_operational_incomes

costing_package/version.py

@@ -1,8 +0,0 @@
-"""
-Cost version number
-SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
-Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
-Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
-"""
-__version__ = '0.1.0.5'

energy_system_modelling_package/energy_system_modelling_factories/archetypes/montreal/archetype_cluster_1.py

@@ -1,147 +0,0 @@
-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
-from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.pv_model import PVModel
-from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.electricity_demand_calculator import HourlyElectricityDemand
-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()
-    if 'PV' in self.building.energy_systems_archetype_name:
-      self.pv_results = self.pv_system_simulation()
-    else:
-      self.pv_results = None
-
-  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 = 11
-    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 pv_system_simulation(self):
-    results = None
-    pv = self.building.energy_systems[0].generation_systems[0]
-    hourly_electricity_demand = HourlyElectricityDemand(self.building).calculate()
-    model_type = 'fixed_efficiency'
-    if model_type == 'fixed_efficiency':
-      results = PVModel(pv=pv,
-                        hourly_electricity_demand_joules=hourly_electricity_demand,
-                        solar_radiation=self.building.roofs[0].global_irradiance_tilted[cte.HOUR],
-                        installed_pv_area=self.building.roofs[0].installed_solar_collector_area,
-                        model_type='fixed_efficiency').fixed_efficiency()
-    return results
-
-  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.pv_results is not None:
-      self.building.onsite_electrical_production[cte.HOUR] = [x * cte.WATTS_HOUR_TO_JULES for x in
-                                                              self.pv_results['PV Output (W)']]
-      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])]
-      if self.csv_output:
-        file_name = f'pv_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(self.pv_results.keys())
-          # Write data
-          output_file.writerows(zip(*self.pv_results.values()))
-    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

@@ -1,79 +0,0 @@
-"""
-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.optimal_sizing import \
-  OptimalSizing
-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.pv_assessment.pv_sizing import PVSizing
-
-
-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 _optimal_sizing(self):
-    """
-    Size Energy Systems using a Single or Multi Objective GA
-    """
-    OptimalSizing(self._city, optimization_scenario='cost_energy_consumption').enrich_buildings()
-    self._city.level_of_detail.energy_systems = 1
-    for building in self._city.buildings:
-      building.level_of_detail.energy_systems = 1
-
-  def _pv_sizing(self):
-    """
-    Size rooftop, facade or mixture of them for buildings
-    """
-    system_type = 'rooftop'
-    results = {}
-    if system_type == 'rooftop':
-      surface_azimuth = 180
-      maintenance_factor = 0.1
-      mechanical_equipment_factor = 0.3
-      orientation_factor = 0.1
-      tilt_angle = self._city.latitude
-      pv_sizing = PVSizing(self._city,
-                           tilt_angle=tilt_angle,
-                           surface_azimuth=surface_azimuth,
-                           mechanical_equipment_factor=mechanical_equipment_factor,
-                           maintenance_factor=maintenance_factor,
-                           orientation_factor=orientation_factor,
-                           system_type=system_type)
-      results = pv_sizing.rooftop_sizing()
-      pv_sizing.rooftop_tilted_radiation()
-
-    self._city.level_of_detail.energy_systems = 1
-    for building in self._city.buildings:
-      building.level_of_detail.energy_systems = 1
-    return results
-
-  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

@@ -1,120 +0,0 @@
-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=None):
-    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 = [0] + [x for x in source_temperature_hourly for _ in range(number_of_ts)]
-    demand = [0] + [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] = 65
-    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
-    if len(self.dhw_demand) == 8760:
-      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
-      if len(self.dhw_demand) == 8760:
-        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

@@ -1,171 +0,0 @@
-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=None):
-    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]
-    if heating_peak_load_watts is not None:
-      self.heating_peak_load = heating_peak_load_watts
-    else:
-      self.heating_peak_load = max(hourly_heating_demand_joules) / cte.HOUR_TO_SECONDS
-    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
-    boiler.energy_consumption[cte.HEATING] = {}
-    boiler.energy_consumption[cte.HEATING][cte.HOUR] = boiler_hourly
-    if len(self.heating_demand) == 8760:
-      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][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] = {}
-      if len(self.heating_demand) == 8760:
-        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

@@ -1,54 +0,0 @@
-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)) / 3.41214
-      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

@@ -1,89 +0,0 @@
-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] / hp_cop[i]
-      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_supply_temperature_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_supply_temperature_hourly.append(t_sup_hp[i])
-        hp_sum = 0
-    self.hp.cooling_supply_temperature = hp_supply_temperature_hourly
-    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 Cooling 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

@@ -1,47 +0,0 @@
-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

@@ -1,36 +0,0 @@
-"""
-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.archetypes.montreal.archetype_cluster_1 import ArchetypeCluster1
-
-
-class MontrealEnergySystemArchetypesSimulationFactory:
-  """
-  EnergySystemsFactory class
-  """
-
-  def __init__(self, handler, building, output_path, csv_output=True):
-    self._output_path = output_path
-    self._handler = '_' + handler.lower()
-    self._building = building
-    self._csv_output = csv_output
-
-  def _archetype_cluster_1(self):
-    """
-    Enrich the city by using the sizing and simulation model developed for archetype13 of montreal_future_systems
-    """
-    dt = 900
-    ArchetypeCluster1(self._building, dt, self._output_path, self._csv_output).enrich_building()
-    self._building.level_of_detail.energy_systems = 2
-    self._building.level_of_detail.energy_systems = 2
-
-  def enrich(self):
-    """
-    Enrich the city given to the class using the class given handler
-    :return: None
-    """
-    getattr(self, self._handler, lambda: None)()

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/electricity_demand_calculator.py

@@ -1,73 +0,0 @@
-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]
-    lighting = self.building.lighting_electrical_demand[cte.HOUR]
-    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(len(self.building.heating_demand[cte.HOUR])):
-      hourly = 0
-      hourly += appliance[i]
-      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_feasibility.py

@@ -1,37 +0,0 @@
-from pathlib import Path
-import subprocess
-from hub.imports.geometry_factory import GeometryFactory
-from building_modelling.geojson_creator import process_geojson
-from hub.helpers.dictionaries import Dictionaries
-from hub.imports.weather_factory import WeatherFactory
-from hub.imports.results_factory import ResultFactory
-from hub.exports.exports_factory import ExportsFactory
-
-
-def pv_feasibility(current_x, current_y, current_diff, selected_buildings):
-  input_files_path = (Path(__file__).parent.parent.parent.parent / 'input_files')
-  output_path = (Path(__file__).parent.parent.parent.parent / 'out_files').resolve()
-  sra_output_path = output_path / 'sra_outputs' / 'extended_city_sra_outputs'
-  sra_output_path.mkdir(parents=True, exist_ok=True)
-  new_diff = current_diff * 5
-  geojson_file = process_geojson(x=current_x, y=current_y, diff=new_diff, expansion=True)
-  file_path = input_files_path / 'output_buildings.geojson'
-  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
-  WeatherFactory('epw', city).enrich()
-  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, sra_output_path).enrich()
-  for selected_building in selected_buildings:
-    for building in city.buildings:
-      if selected_building.name == building.name:
-        selected_building.roofs[0].global_irradiance = building.roofs[0].global_irradiance
-
-
-
-

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/pv_model.py

@@ -1,42 +0,0 @@
-import math
-import hub.helpers.constants as cte
-from hub.helpers.monthly_values import MonthlyValues
-
-
-class PVModel:
-  def __init__(self, pv, hourly_electricity_demand_joules, solar_radiation, installed_pv_area, model_type, ns=None,
-               np=None):
-    self.pv = pv
-    self.hourly_electricity_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in hourly_electricity_demand_joules]
-    self.solar_radiation = solar_radiation
-    self.installed_pv_area = installed_pv_area
-    self._model_type = '_' + model_type.lower()
-    self.ns = ns
-    self.np = np
-    self.results = {}
-
-  def fixed_efficiency(self):
-    module_efficiency = float(self.pv.electricity_efficiency)
-    variable_names = ["pv_output", "import", "export", "self_sufficiency_ratio"]
-    variables = {name: [0] * len(self.hourly_electricity_demand) for name in variable_names}
-    (pv_out, grid_import, grid_export, self_sufficiency_ratio) = [variables[name] for name in variable_names]
-    for i in range(len(self.hourly_electricity_demand)):
-      pv_out[i] = module_efficiency * self.installed_pv_area * self.solar_radiation[i] / cte.WATTS_HOUR_TO_JULES
-      if pv_out[i] < self.hourly_electricity_demand[i]:
-        grid_import[i] = self.hourly_electricity_demand[i] - pv_out[i]
-      else:
-        grid_export[i] = pv_out[i] - self.hourly_electricity_demand[i]
-      self_sufficiency_ratio[i] = pv_out[i] / self.hourly_electricity_demand[i]
-    self.results['Electricity Demand (W)'] = self.hourly_electricity_demand
-    self.results['PV Output (W)'] = pv_out
-    self.results['Imported from Grid (W)'] = grid_import
-    self.results['Exported to Grid (W)'] = grid_export
-    self.results['Self Sufficiency Ratio'] = self_sufficiency_ratio
-    return self.results
-
-  def enrich(self):
-    """
-    Enrich the city given to the class using the class given handler
-    :return: None
-    """
-    return getattr(self, self._model_type, lambda: None)()

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/pv_sizing.py

@@ -1,70 +0,0 @@
-import math
-import hub.helpers.constants as cte
-from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.solar_angles import CitySolarAngles
-from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.radiation_tilted import RadiationTilted
-
-
-class PVSizing(CitySolarAngles):
-  def __init__(self, city, tilt_angle, surface_azimuth=180, maintenance_factor=0.1, mechanical_equipment_factor=0.3,
-               orientation_factor=0.1, system_type='rooftop'):
-    super().__init__(location_latitude=city.latitude,
-                     location_longitude=city.longitude,
-                     tilt_angle=tilt_angle,
-                     surface_azimuth_angle=surface_azimuth)
-    self.city = city
-    self.maintenance_factor = maintenance_factor
-    self.mechanical_equipment_factor = mechanical_equipment_factor
-    self.orientation_factor = orientation_factor
-    self.angles = self.calculate
-    self.system_type = system_type
-
-  def rooftop_sizing(self):
-    results = {}
-    # Available Roof Area
-    for building in self.city.buildings:
-      for energy_system in building.energy_systems:
-        for generation_system in energy_system.generation_systems:
-          if generation_system.system_type == cte.PHOTOVOLTAIC:
-            module_width = float(generation_system.width)
-            module_height = float(generation_system.height)
-            roof_area = 0
-            for roof in building.roofs:
-              roof_area += roof.perimeter_area
-            pv_module_area = module_width * module_height
-            available_roof = ((self.maintenance_factor + self.orientation_factor + self.mechanical_equipment_factor) *
-                              roof_area)
-        # Inter-Row Spacing
-            winter_solstice = self.angles[(self.angles['AST'].dt.month == 12) &
-                                          (self.angles['AST'].dt.day == 21) &
-                                          (self.angles['AST'].dt.hour == 12)]
-            solar_altitude = winter_solstice['solar altitude'].values[0]
-            solar_azimuth = winter_solstice['solar azimuth'].values[0]
-            distance = ((module_height * abs(math.cos(math.radians(solar_azimuth)))) /
-                        math.tan(math.radians(solar_altitude)))
-            distance = float(format(distance, '.1f'))
-        # 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 = panels_per_row * number_of_rows * pv_module_area
-            building.roofs[0].installed_solar_collector_area = total_pv_area
-            results[f'Building {building.name}'] = {'total_roof_area': roof_area,
-                                                    'PV dedicated area': available_roof,
-                                                    'total_pv_area': total_pv_area,
-                                                    'total_number_of_panels': total_number_of_panels,
-                                                    'number_of_rows': number_of_rows,
-                                                    'panels_per_row': panels_per_row}
-    return results
-
-  def rooftop_tilted_radiation(self):
-    for building in self.city.buildings:
-      RadiationTilted(building=building,
-                      solar_angles=self.angles,
-                      tilt_angle=self.tilt_angle,
-                      ghi=building.roofs[0].global_irradiance[cte.HOUR],
-                      ).enrich()
-
-  def facade_sizing(self):
-    pass

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/pv_sizing_and_simulation.py

@@ -1,59 +0,0 @@
-import math
-
-from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.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_constant_efficiency(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)
-    self.building.roofs[0].installed_solar_collector_area = pv_module_area * self.total_number_of_panels
-    system_efficiency = 0.2
-    pv_hourly_production = [x * system_efficiency * self.total_number_of_panels * pv_module_area *
-                            cte.WATTS_HOUR_TO_JULES 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])]

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/radiation_tilted.py

@@ -1,110 +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] = 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])]
-
-
-

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/solar_angles.py

@@ -1,145 +0,0 @@
-import math
-import pandas as pd
-from datetime import datetime
-from pathlib import Path
-
-
-class CitySolarAngles:
-  def __init__(self, location_latitude, location_longitude, tilt_angle, surface_azimuth_angle,
-               standard_meridian=-75):
-    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
-
-
-
-
-
-
-
-

energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/genetic_algorithm/individual.py

@@ -1,470 +0,0 @@
-import math
-import random
-from hub.helpers.dictionaries import Dictionaries
-from hub.catalog_factories.costs_catalog_factory import CostsCatalogFactory
-import hub.helpers.constants as cte
-from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.domestic_hot_water_heat_pump_with_tes import \
-  DomesticHotWaterHeatPumpTes
-from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.heat_pump_boiler_tes_heating import \
-  HeatPumpBoilerTesHeating
-import numpy_financial as npf
-
-
-class Individual:
-  def __init__(self, building, energy_system, design_period_energy_demands, optimization_scenario,
-               heating_design_load=None, cooling_design_load=None, dt=900, fuel_price_index=0.05,
-               electricity_tariff_type='fixed', consumer_price_index=0.04, interest_rate=0.04,
-               discount_rate=0.03, percentage_credit=0, credit_years=15):
-    """
-    :param building: building object
-    :param energy_system: energy system to be optimized
-    :param design_period_energy_demands: A dictionary of design period heating, cooling and dhw demands. Design period
-    is the day with the highest total demand and the two days before and after it
-    :param optimization_scenario: a string indicating the objective function from minimization of cost,
-    energy consumption, and both together
-    :param heating_design_load: heating design load in W
-    :param cooling_design_load: cooling design load in W
-    :param dt the time step size used for simulations
-    :param fuel_price_index the price increase index of all fuels. A single value is used for all fuels.
-    :param electricity_tariff_type the electricity tariff type between 'fixed' and 'variable' for economic optimization
-    :param consumer_price_index
-    """
-    self.building = building
-    self.energy_system = energy_system
-    self.design_period_energy_demands = design_period_energy_demands
-    self.demand_types = energy_system.demand_types
-    self.optimization_scenario = optimization_scenario
-    self.heating_design_load = heating_design_load
-    self.cooling_design_load = cooling_design_load
-    self.available_space = building.volume / building.storeys_above_ground
-    self.dt = dt
-    self.fuel_price_index = fuel_price_index
-    self.electricity_tariff_type = electricity_tariff_type
-    self.consumer_price_index = consumer_price_index
-    self.interest_rate = interest_rate
-    self.discount_rate = discount_rate
-    self.credit_years = credit_years
-    self.percentage_credit = percentage_credit
-    self.costs = self.costs_archetype()
-    self.feasibility = True
-    self.fitness_score = 0
-    self.individual = {}
-
-  def system_components(self):
-    """
-    Extracts system components (generation and storage) for a given energy system.
-    :return: Dictionary of system components.
-    """
-    self.individual['Generation Components'] = []
-    self.individual['Energy Storage Components'] = []
-    self.individual['End of Life Cost'] = self.costs.end_of_life_cost
-    for generation_component in self.energy_system.generation_systems:
-      investment_cost, reposition_cost, lifetime = self.unit_investment_cost('Generation',
-                                                                             generation_component.system_type)
-      maintenance_cost = self.unit_maintenance_cost(generation_component)
-      if generation_component.system_type == cte.PHOTOVOLTAIC:
-        heating_capacity = None
-        cooling_capacity = None
-        heat_efficiency = None
-        cooling_efficiency = None
-        unit_fuel_cost = 0
-      else:
-        heating_capacity = 0
-        cooling_capacity = 0
-        heat_efficiency = generation_component.heat_efficiency
-        cooling_efficiency = generation_component.cooling_efficiency
-        unit_fuel_cost = self.fuel_cost_per_kwh(generation_component.fuel_type, 'fixed')
-      self.individual['Generation Components'].append({
-        'type': generation_component.system_type,
-        'heating_capacity': heating_capacity,
-        'cooling_capacity': cooling_capacity,
-        'electricity_capacity': 0,
-        'nominal_heating_efficiency': heat_efficiency,
-        'nominal_cooling_efficiency': cooling_efficiency,
-        'nominal_electricity_efficiency': generation_component.electricity_efficiency,
-        'fuel_type': generation_component.fuel_type,
-        'unit_investment_cost': investment_cost,
-        'unit_reposition_cost': reposition_cost,
-        'unit_fuel_cost(CAD/kWh)': unit_fuel_cost,
-        'unit_maintenance_cost': maintenance_cost,
-        'lifetime': lifetime
-      })
-      if generation_component.energy_storage_systems is not None:
-        for energy_storage_system in generation_component.energy_storage_systems:
-          investment_cost, reposition_cost, lifetime = (
-            self.unit_investment_cost('Storage',
-                                      f'{energy_storage_system.type_energy_stored}_storage'))
-          if energy_storage_system.type_energy_stored == cte.THERMAL:
-            heating_coil_capacity = energy_storage_system.heating_coil_capacity
-            heating_coil_fuel_cost = self.fuel_cost_per_kwh(f'{cte.ELECTRICITY}', 'fixed')
-            volume = 0
-            capacity = None
-          else:
-            heating_coil_capacity = None
-            heating_coil_fuel_cost = None
-            volume = None
-            capacity = 0
-          self.individual['Energy Storage Components'].append(
-            {'type': f'{energy_storage_system.type_energy_stored}_storage',
-             'capacity': capacity,
-             'volume': volume,
-             'heating_coil_capacity': heating_coil_capacity,
-             'unit_investment_cost': investment_cost,
-             'unit_reposition_cost': reposition_cost,
-             'heating_coil_fuel_cost': heating_coil_fuel_cost,
-             'unit_maintenance_cost': 0,
-             'lifetime': lifetime
-             })
-
-  def initialization(self):
-    """
-    Assigns initial sizes to generation and storage components based on heating and cooling design loads and
-    available space in the building.
-    :return:
-    """
-    self.system_components()
-    generation_components = self.individual['Generation Components']
-    storage_components = self.individual['Energy Storage Components']
-    for generation_component in generation_components:
-      if generation_component[
-        'nominal_heating_efficiency'] is not None and cte.HEATING or cte.DOMESTIC_HOT_WATER in self.demand_types:
-        if self.heating_design_load is not None:
-          generation_component['heating_capacity'] = random.uniform(0, self.heating_design_load)
-        else:
-          if cte.HEATING in self.demand_types:
-            generation_component['heating_capacity'] = random.uniform(0, max(
-              self.design_period_energy_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES)
-          else:
-            generation_component['heating_capacity'] = random.uniform(0, max(
-              self.design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES)
-      else:
-        generation_component['heating_capacity'] = None
-      if generation_component['nominal_cooling_efficiency'] is not None and cte.COOLING in self.demand_types:
-        if self.cooling_design_load is not None:
-          generation_component['cooling_capacity'] = random.uniform(0, self.cooling_design_load)
-        else:
-          generation_component['cooling_capacity'] = random.uniform(0, max(
-            self.design_period_energy_demands[cte.COOLING]['demands']) / cte.WATTS_HOUR_TO_JULES)
-      else:
-        generation_component['cooling_capacity'] = None
-      if generation_component['nominal_electricity_efficiency'] is None:
-        generation_component['electricity_capacity'] = None
-    for storage_component in storage_components:
-      if storage_component['type'] == f'{cte.THERMAL}_storage':
-        storage_component['volume'] = random.uniform(0, 0.01 * self.available_space)
-        if storage_component['heating_coil_capacity'] is not None:
-          if self.heating_design_load is not None:
-            storage_component['heating_coil_capacity'] = random.uniform(0, self.heating_design_load)
-          else:
-            if cte.HEATING in self.demand_types:
-              storage_component['heating_coil_capacity'] = random.uniform(0, max(
-                self.design_period_energy_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES)
-            else:
-              storage_component['heating_coil_capacity'] = random.uniform(0, max(
-                self.design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES)
-
-  def score_evaluation(self):
-    self.system_simulation()
-    self.individual['feasible'] = self.feasibility
-    lcc = 0
-    total_energy_consumption = 0
-    if self.feasibility:
-      if 'cost' in self.optimization_scenario:
-        investment_cost = 0
-        operation_cost_year_0 = 0
-        maintenance_cost_year_0 = 0
-        for generation_system in self.individual['Generation Components']:
-          heating_capacity = 0 if generation_system['heating_capacity'] is None else generation_system[
-            'heating_capacity']
-          cooling_capacity = 0 if generation_system['cooling_capacity'] is None else generation_system[
-            'cooling_capacity']
-          capacity = max(heating_capacity, cooling_capacity)
-          investment_cost += capacity * generation_system['unit_investment_cost'] / 1000
-          maintenance_cost_year_0 += capacity * generation_system['unit_maintenance_cost'] / 1000
-          operation_cost_year_0 += (generation_system['total_energy_consumption(kWh)'] *
-                                    generation_system['unit_fuel_cost(CAD/kWh)'])
-        for storage_system in self.individual['Energy Storage Components']:
-          if cte.THERMAL in storage_system['type']:
-            investment_cost += storage_system['volume'] * storage_system['unit_investment_cost']
-            if storage_system['heating_coil_capacity'] is not None:
-              operation_cost_year_0 += (storage_system['total_energy_consumption(kWh)'] *
-                                        storage_system['heating_coil_fuel_cost'])
-        lcc = self.life_cycle_cost_calculation(investment_cost=investment_cost,
-                                               operation_cost_year_0=operation_cost_year_0,
-                                               maintenance_cost_year_0=maintenance_cost_year_0)
-        self.individual['lcc'] = lcc
-      if 'energy_consumption' in self.optimization_scenario:
-        total_energy_consumption = 0
-        for generation_system in self.individual['Generation Components']:
-          total_energy_consumption += generation_system['total_energy_consumption(kWh)']
-        for storage_system in self.individual['Energy Storage Components']:
-          if cte.THERMAL in storage_system['type'] and storage_system['heating_coil_capacity'] is not None:
-            total_energy_consumption += storage_system['total_energy_consumption(kWh)']
-        self.individual['total_energy_consumption'] = total_energy_consumption
-    # Fitness score based on the optimization scenario
-      if self.optimization_scenario == 'cost':
-        self.fitness_score = lcc
-        self.individual['fitness_score'] = lcc
-      elif self.optimization_scenario == 'energy_consumption':
-        self.fitness_score = total_energy_consumption
-        self.individual['fitness_score'] = total_energy_consumption
-      elif self.optimization_scenario == 'cost_energy_consumption' or self.optimization_scenario == 'energy_consumption_cost':
-        self.fitness_score = (lcc, total_energy_consumption)
-        self.individual['fitness_score'] = (lcc, total_energy_consumption)
-    else:
-      lcc = float('inf')
-      total_energy_consumption = float('inf')
-      self.individual['lcc'] = lcc
-      self.individual['total_energy_consumption'] = total_energy_consumption
-      if self.optimization_scenario == 'cost_energy_consumption' or self.optimization_scenario == 'energy_consumption_cost':
-        self.individual['fitness_score'] = (float('inf'), float('inf'))
-        self.fitness_score = (float('inf'), float('inf'))
-      else:
-        self.individual['fitness_score'] = float('inf')
-        self.fitness_score = float('inf')
-
-  def system_simulation(self):
-    """
-    The method to run the energy system model using the existing models in the energy_system_modelling_package.
-    Based on cluster id and demands, model is imported and run.
-    :return: dictionary of results
-    """
-    if self.building.energy_systems_archetype_cluster_id == '1':
-      if cte.HEATING in self.demand_types:
-        boiler = self.energy_system.generation_systems[0]
-        boiler.nominal_heat_output = self.individual['Generation Components'][0]['heating_capacity']
-        hp = self.energy_system.generation_systems[1]
-        hp.nominal_heat_output = self.individual['Generation Components'][1]['heating_capacity']
-        tes = self.energy_system.generation_systems[0].energy_storage_systems[0]
-        tes.volume = self.individual['Energy Storage Components'][0]['volume']
-        tes.height = self.building.average_storey_height - 1
-        tes.heating_coil_capacity = self.individual['Energy Storage Components'][0]['heating_coil_capacity'] \
-          if self.individual['Energy Storage Components'][0]['heating_coil_capacity'] is not None else None
-        heating_demand_joules = self.design_period_energy_demands[cte.HEATING]['demands']
-        heating_peak_load_watts = max(self.design_period_energy_demands[cte.HEATING]) if \
-          (self.heating_design_load is not None) else self.building.heating_peak_load[cte.YEAR][0]
-        upper_limit_tes_heating = 55
-        design_period_start_index = self.design_period_energy_demands[cte.HEATING]['start_index']
-        design_period_end_index = self.design_period_energy_demands[cte.HEATING]['end_index']
-        outdoor_temperature = self.building.external_temperature[cte.HOUR][
-                              design_period_start_index:design_period_end_index]
-        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()
-        if min(results['TES Temperature']) < 35:
-          self.feasibility = False
-      elif cte.DOMESTIC_HOT_WATER in self.demand_types:
-        hp = self.energy_system.generation_systems[0]
-        hp.nominal_heat_output = self.individual['Generation Components'][0]['heating_capacity']
-        tes = self.energy_system.generation_systems[0].energy_storage_systems[0]
-        tes.volume = self.individual['Energy Storage Components'][0]['volume']
-        tes.height = self.building.average_storey_height - 1
-        tes.heating_coil_capacity = self.individual['Energy Storage Components'][0]['heating_coil_capacity'] \
-          if self.individual['Energy Storage Components'][0]['heating_coil_capacity'] is not None else None
-        dhw_demand_joules = self.design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['demands']
-        upper_limit_tes = 65
-        design_period_start_index = self.design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['start_index']
-        design_period_end_index = self.design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['end_index']
-        outdoor_temperature = self.building.external_temperature[cte.HOUR][
-                              design_period_start_index:design_period_end_index]
-        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()
-        if min(results['DHW TES Temperature']) < 55:
-          self.feasibility = False
-      if self.feasibility:
-        generation_system_types = [generation_system.system_type for generation_system in
-                                   self.energy_system.generation_systems]
-        for generation_component in self.individual['Generation Components']:
-          if generation_component['type'] in generation_system_types:
-            index = generation_system_types.index(generation_component['type'])
-            for demand_type in self.demand_types:
-              if demand_type in self.energy_system.generation_systems[index].energy_consumption:
-                generation_component['total_energy_consumption(kWh)'] = (sum(
-                  self.energy_system.generation_systems[index].energy_consumption[demand_type][cte.HOUR]) / 3.6e6)
-        for storage_component in self.individual['Energy Storage Components']:
-          if storage_component['type'] == f'{cte.THERMAL}_storage' and storage_component[
-            'heating_coil_capacity'] is not None:
-            for generation_system in self.energy_system.generation_systems:
-              if generation_system.energy_storage_systems is not None:
-                for storage_system in generation_system.energy_storage_systems:
-                  if storage_system.type_energy_stored == cte.THERMAL:
-                    for demand_type in self.demand_types:
-                      if demand_type in storage_system.heating_coil_energy_consumption:
-                        storage_component['total_energy_consumption(kWh)'] = (sum(
-                          storage_system.heating_coil_energy_consumption[demand_type][cte.HOUR]) / 3.6e6)
-
-  def life_cycle_cost_calculation(self, investment_cost, operation_cost_year_0, maintenance_cost_year_0,
-                                  life_cycle_duration=41):
-    """
-    Calculating the life cycle cost of the energy system. The original cost workflow in hub is not used to reduce
-    computation time.Here are the steps:
-        1- Costs catalog and different components are imported.
-        2- Capital costs (investment and reposition) are calculated and appended to a list to have the capital cash
-           flow.
-        3-
-    :param maintenance_cost_year_0:
-    :param operation_cost_year_0:
-    :param investment_cost:
-    :param life_cycle_duration:
-    :return:
-    """
-    capital_costs_cash_flow = [investment_cost]
-    operational_costs_cash_flow = [0]
-    maintenance_costs_cash_flow = [0]
-    end_of_life_costs = [0] * (life_cycle_duration + 1)
-    for i in range(1, life_cycle_duration + 1):
-      yearly_operational_cost = math.pow(1 + self.fuel_price_index, i) * operation_cost_year_0
-      yearly_maintenance_cost = math.pow(1 + self.consumer_price_index, i) * maintenance_cost_year_0
-      yearly_capital_cost = 0
-      for generation_system in self.individual['Generation Components']:
-        if (i % generation_system['lifetime']) == 0 and i != (life_cycle_duration - 1):
-          cost_increase = math.pow(1 + self.consumer_price_index, i)
-          heating_capacity = 0 if generation_system['heating_capacity'] is None else generation_system[
-            'heating_capacity']
-          cooling_capacity = 0 if generation_system['cooling_capacity'] is None else generation_system[
-            'cooling_capacity']
-          capacity = max(heating_capacity, cooling_capacity)
-          yearly_capital_cost += generation_system['unit_reposition_cost'] * capacity * cost_increase / 1000
-          yearly_capital_cost += -npf.pmt(self.interest_rate, self.credit_years,
-                                          investment_cost * self.percentage_credit)
-      for storage_system in self.individual['Energy Storage Components']:
-        if (i % storage_system['lifetime']) == 0 and i != (life_cycle_duration - 1):
-          cost_increase = math.pow(1 + self.consumer_price_index, i)
-          capacity = storage_system['volume'] if cte.THERMAL in storage_system['type'] else storage_system['capacity']
-          yearly_capital_cost += storage_system['unit_reposition_cost'] * capacity * cost_increase
-          yearly_capital_cost += -npf.pmt(self.interest_rate, self.credit_years,
-                                          investment_cost * self.percentage_credit)
-      capital_costs_cash_flow.append(yearly_capital_cost)
-      operational_costs_cash_flow.append(yearly_operational_cost)
-      maintenance_costs_cash_flow.append(yearly_maintenance_cost)
-    for year in range(1, life_cycle_duration + 1):
-      price_increase = math.pow(1 + self.consumer_price_index, year)
-      if year == life_cycle_duration:
-        end_of_life_costs[year] = (
-            self.building.thermal_zones_from_internal_zones[0].total_floor_area *
-            self.individual['End of Life Cost'] * price_increase
-        )
-
-    life_cycle_capital_cost = npf.npv(self.discount_rate, capital_costs_cash_flow)
-    life_cycle_operational_cost = npf.npv(self.discount_rate, operational_costs_cash_flow)
-    life_cycle_maintenance_cost = npf.npv(self.discount_rate, maintenance_costs_cash_flow)
-    life_cycle_end_of_life_cost = npf.npv(self.discount_rate, end_of_life_costs)
-    total_life_cycle_cost = life_cycle_capital_cost + life_cycle_operational_cost + life_cycle_maintenance_cost + life_cycle_end_of_life_cost
-    return total_life_cycle_cost
-
-  def costs_archetype(self):
-    costs_catalogue = CostsCatalogFactory('montreal_new').catalog.entries().archetypes
-    dictionary = Dictionaries().hub_function_to_montreal_custom_costs_function
-    costs_archetype = None
-    for archetype in costs_catalogue:
-      if dictionary[str(self.building.function)] == str(archetype.function):
-        costs_archetype = archetype
-    return costs_archetype
-
-  def unit_investment_cost(self, component_category, component_type):
-    """
-    Reading the investment and reposition costs of any component from costs catalogue
-    :param component_category: Due to the categorizations in the cost catalogue, we need this parameter to realize if
-    the component is a generation or storage component
-    :param component_type: Type of the component
-    :return:
-    """
-    investment_cost = 0
-    reposition_cost = 0
-    lifetime = 0
-    name = ''
-    capital_costs_chapter = self.costs.capital_cost.chapter('D_services')
-    if component_category == 'Generation':
-      generation_systems = self.energy_system.generation_systems
-      for generation_system in generation_systems:
-        if component_type == generation_system.system_type:
-          if generation_system.system_type == cte.HEAT_PUMP:
-            name += (
-                generation_system.source_medium.lower() + '_to_' + generation_system.supply_medium.lower() + '_' +
-                generation_system.system_type.lower().replace(' ', '_'))
-          elif generation_system.system_type == cte.BOILER:
-            if generation_system.fuel_type == cte.ELECTRICITY:
-              name += cte.ELECTRICAL.lower() + f'_{generation_system.system_type}'.lower()
-            else:
-              name += generation_system.fuel_type.lower() + f'_{generation_system.system_type}'.lower()
-          elif generation_system.system_type == cte.PHOTOVOLTAIC:
-            name += 'D2010_photovoltaic_system'
-          else:
-            if cte.HEATING or cte.DOMESTIC_HOT_WATER in self.demand_types:
-              name += 'template_heat'
-            else:
-              name += 'template_cooling'
-      for item in capital_costs_chapter.items:
-        if name in item.type:
-          investment_cost += float(capital_costs_chapter.item(item.type).initial_investment[0])
-          reposition_cost += float(capital_costs_chapter.item(item.type).reposition[0])
-          lifetime += float(capital_costs_chapter.item(item.type).lifetime)
-    elif component_category == 'Storage':
-      for generation_system in self.energy_system.generation_systems:
-        if generation_system.energy_storage_systems is not None:
-          for energy_storage_system in generation_system.energy_storage_systems:
-            if energy_storage_system.type_energy_stored == cte.THERMAL:
-              if energy_storage_system.heating_coil_capacity is not None:
-                investment_cost += float(capital_costs_chapter.item('D306010_storage_tank').initial_investment[0])
-                reposition_cost += float(capital_costs_chapter.item('D306010_storage_tank').reposition[0])
-                lifetime += float(capital_costs_chapter.item('D306010_storage_tank').lifetime)
-              else:
-                investment_cost += float(
-                  capital_costs_chapter.item('D306020_storage_tank_with_coil').initial_investment[0])
-                reposition_cost += float(capital_costs_chapter.item('D306020_storage_tank_with_coil').reposition[0])
-                lifetime += float(capital_costs_chapter.item('D306020_storage_tank_with_coil').lifetime)
-
-    return investment_cost, reposition_cost, lifetime
-
-  def unit_maintenance_cost(self, generation_system):
-    hvac_maintenance = self.costs.operational_cost.maintenance_hvac
-    pv_maintenance = self.costs.operational_cost.maintenance_pv
-    maintenance_cost = 0
-    component = None
-    if generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.AIR:
-      component = 'air_source_heat_pump'
-    elif generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.GROUND:
-      component = 'ground_source_heat_pump'
-    elif generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.WATER:
-      component = 'water_source_heat_pump'
-    elif generation_system.system_type == cte.BOILER and generation_system.fuel_type == cte.GAS:
-      component = 'gas_boiler'
-    elif generation_system.system_type == cte.BOILER and generation_system.fuel_type == cte.ELECTRICITY:
-      component = 'electric_boiler'
-    elif generation_system.system_type == cte.PHOTOVOLTAIC:
-      maintenance_cost += pv_maintenance
-    else:
-      if cte.HEATING or cte.DOMESTIC_HOT_WATER in self.demand_types:
-        component = 'general_heating_equipment'
-      else:
-        component = 'general_cooling_equipment'
-    for item in hvac_maintenance:
-      if item.type == component:
-        maintenance_cost += item.maintenance[0]
-    return maintenance_cost
-
-  def fuel_cost_per_kwh(self, fuel_type, fuel_cost_tariff_type):
-    fuel_cost = 0
-    catalog_fuels = self.costs.operational_cost.fuels
-    for fuel in catalog_fuels:
-      if fuel_type == fuel.type and fuel_cost_tariff_type == fuel.variable.rate_type:
-        if fuel.type == cte.ELECTRICITY and fuel_cost_tariff_type == 'fixed':
-          fuel_cost = fuel.variable.values[0]
-        elif fuel.type == cte.ELECTRICITY and fuel_cost_tariff_type == 'variable':
-          fuel_cost = fuel.variable.values[0]
-        else:
-          if fuel.type == cte.BIOMASS:
-            conversion_factor = 1
-          else:
-            conversion_factor = fuel.density[0]
-          fuel_cost = fuel.variable.values[0] / (conversion_factor * fuel.lower_heating_value[0] * 0.277)
-    return fuel_cost

energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/genetic_algorithm/multi_objective_genetic_algorithm.py

@@ -1,418 +0,0 @@
-import copy
-import math
-import random
-import hub.helpers.constants as cte
-from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.genetic_algorithm.individual import \
-  Individual
-import matplotlib.pyplot as plt
-
-
-class MultiObjectiveGeneticAlgorithm:
-  def __init__(self, population_size=100, generations=50, crossover_rate=0.8, mutation_rate=0.1,
-               optimization_scenario=None, output_path=None):
-    self.population_size = population_size
-    self.population = []
-    self.generations = generations
-    self.crossover_rate = crossover_rate
-    self.mutation_rate = mutation_rate
-    self.optimization_scenario = optimization_scenario
-    self.list_of_solutions = []
-    self.best_solution = None
-    self.best_solution_generation = None
-    self.output_path = output_path
-
-# Initialize Population
-  def initialize_population(self, building, energy_system):
-    design_period_energy_demands = self.design_period_identification(building)
-    attempts = 0
-    max_attempts = self.population_size * 5
-    while len(self.population) < self.population_size and attempts < max_attempts:
-      individual = Individual(building=building,
-                              energy_system=energy_system,
-                              design_period_energy_demands=design_period_energy_demands,
-                              optimization_scenario=self.optimization_scenario)
-      individual.initialization()
-      attempts += 1
-      if self.initial_population_feasibility_check(individual, energy_system.demand_types,
-                                                   design_period_energy_demands):
-        self.population.append(individual)
-    if len(self.population) < self.population_size:
-      raise RuntimeError(f"Could not generate a feasible population of size {self.population_size}. "
-                         f"Only {len(self.population)} feasible individuals were generated.")
-
-  @staticmethod
-  def initial_population_feasibility_check(individual, demand_types, design_period_demands):
-    total_heating_capacity = sum(
-      component['heating_capacity'] for component in individual.individual['Generation Components']
-      if component['heating_capacity'] is not None
-    )
-    total_cooling_capacity = sum(
-      component['cooling_capacity'] for component in individual.individual['Generation Components']
-      if component['cooling_capacity'] is not None
-    )
-    max_heating_demand = max(design_period_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES
-    max_cooling_demand = max(design_period_demands[cte.COOLING]['demands']) / cte.WATTS_HOUR_TO_JULES
-    max_dhw_demand = max(design_period_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES
-    if cte.HEATING in demand_types and total_heating_capacity < 0.5 * max_heating_demand:
-      return False
-    if cte.DOMESTIC_HOT_WATER in demand_types and total_heating_capacity < 0.5 * max_dhw_demand:
-      return False
-    if cte.COOLING in demand_types and total_cooling_capacity < 0.5 * max_cooling_demand:
-      return False
-    total_volume = sum(
-      component['volume'] for component in individual.individual['Energy Storage Components']
-      if component['volume'] is not None
-    )
-    max_storage_volume = individual.available_space * 0.1
-    if total_volume > max_storage_volume:
-      return False
-    return True
-
-  def nsga2_selection(self, population, fronts, crowding_distances):
-    new_population = []
-    i = 0
-    while len(new_population) + len(fronts[i]) <= self.population_size:
-      for index in fronts[i]:
-        # Skip individuals with infinite fitness values to avoid propagation
-        if not math.isinf(self.population[index].individual['fitness_score'][0]) and \
-            not math.isinf(self.population[index].individual['fitness_score'][1]):
-          new_population.append(population[index])
-      i += 1
-      if i >= len(fronts):
-        break
-    if len(new_population) < self.population_size and i < len(fronts):
-      sorted_front = sorted(fronts[i], key=lambda x: crowding_distances[i][x], reverse=True)
-      for index in sorted_front:
-        if len(new_population) < self.population_size:
-          if not math.isinf(self.population[index].individual['fitness_score'][0]) and \
-              not math.isinf(self.population[index].individual['fitness_score'][1]):
-            new_population.append(population[index])
-        else:
-          break
-    return new_population
-
-  def fast_non_dominated_sort(self):
-    population_size = self.population_size
-    s = [[] for _ in range(population_size)]
-    front = [[]]
-    n = [0] * population_size
-    rank = [0] * population_size
-    for p in range(population_size):
-      s[p] = []
-      n[p] = 0
-      for q in range(population_size):
-        if self.dominates(self.population[p], self.population[q]):
-          s[p].append(q)
-        elif self.dominates(self.population[q], self.population[p]):
-          n[p] += 1
-      if n[p] == 0:
-        rank[p] = 0
-        front[0].append(p)
-    i = 0
-    while front[i]:
-      next_front = set()
-      for p in front[i]:
-        for q in s[p]:
-          n[q] -= 1
-          if n[q] == 0:
-            rank[q] = i + 1
-            next_front.add(q)
-      i += 1
-      front.append(list(next_front))
-    del front[-1]
-    return front
-
-  @staticmethod
-  def dominates(individual1, individual2):
-    lcc1, lce1 = individual1.individual['fitness_score']
-    lcc2, lce2 = individual2.individual['fitness_score']
-    return (lcc1 <= lcc2 and lce1 <= lce2) and (lcc1 < lcc2 or lce1 < lce2)
-
-  def calculate_crowding_distance(self, front):
-    crowding_distance = [0] * len(self.population)
-    for objective in ['lcc', 'total_energy_consumption']:
-      sorted_front = sorted(front, key=lambda x: self.population[x].individual[objective])
-      # Set distances to finite large numbers rather than `inf`
-      crowding_distance[sorted_front[0]] = float(1e9)
-      crowding_distance[sorted_front[-1]] = float(1e9)
-      objective_min = self.population[sorted_front[0]].individual[objective]
-      objective_max = self.population[sorted_front[-1]].individual[objective]
-      if objective_max != objective_min:
-        for i in range(1, len(sorted_front) - 1):
-          crowding_distance[sorted_front[i]] += (
-              (self.population[sorted_front[i + 1]].individual[objective] -
-               self.population[sorted_front[i - 1]].individual[objective]) /
-              (objective_max - objective_min))
-    return crowding_distance
-
-  def crossover(self, parent1, parent2):
-    """
-    Crossover between two parents to produce two children.
-    swaps generation components and storage components between the two parents with a 50% chance.
-    :param parent1: First parent individual.
-    :param parent2: second parent individual.
-    :return: Two child individuals (child1 and child2).
-    """
-    if random.random() < self.crossover_rate:
-      # Deep copy of the parents to create children
-      child1, child2 = copy.deepcopy(parent1), copy.deepcopy(parent2)
-      # Crossover for Generation Components
-      for i in range(len(parent1.individual['Generation Components'])):
-        if random.random() < 0.5:
-          # swap the entire generation component
-          child1.individual['Generation Components'][i], child2.individual['Generation Components'][i] = (
-            child2.individual['Generation Components'][i],
-            child1.individual['Generation Components'][i]
-          )
-
-      # Crossover for Energy storage Components
-      for i in range(len(parent1.individual['Energy Storage Components'])):
-        if random.random() < 0.5:
-          # swap the entire storage component
-          child1.individual['Energy Storage Components'][i], child2.individual['Energy Storage Components'][i] = (
-            child2.individual['Energy Storage Components'][i],
-            child1.individual['Energy Storage Components'][i]
-          )
-
-      return child1, child2
-    else:
-      # If crossover does not happen, return copies of the original parents
-      return copy.deepcopy(parent1), copy.deepcopy(parent2)
-
-  def mutate(self, individual, building, energy_system):
-    """
-    Mutates the individual's generation and storage components.
-
-    - `individual`: The individual to mutate (contains generation and storage components).
-    - `building`: Building data that contains constraints such as peak heating load and available space.
-
-    Returns the mutated individual.
-    """
-    design_period_energy_demands = self.design_period_identification(building)
-    # Mutate Generation Components
-    for generation_component in individual['Generation Components']:
-      if random.random() < self.mutation_rate:
-        if (generation_component['nominal_heating_efficiency'] is not None and cte.HEATING or cte.DOMESTIC_HOT_WATER in
-            energy_system.demand_types):
-          # Mutate heating capacity
-          if cte.HEATING in energy_system.demand_types:
-            generation_component['heating_capacity'] = random.uniform(
-              0, max(design_period_energy_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES)
-          else:
-            generation_component['heating_capacity'] = random.uniform(
-              0, max(design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES)
-        if generation_component['nominal_cooling_efficiency'] is not None and cte.COOLING in energy_system.demand_types:
-          # Mutate cooling capacity
-          generation_component['cooling_capacity'] = random.uniform(
-            0, max(design_period_energy_demands[cte.COOLING]['demands']) / cte.WATTS_HOUR_TO_JULES)
-    # Mutate storage Components
-    for storage_component in individual['Energy Storage Components']:
-      if random.random() < self.mutation_rate:
-        if storage_component['type'] == f'{cte.THERMAL}_storage':
-          # Mutate the volume of thermal storage
-          max_available_space = 0.01 * building.volume / building.storeys_above_ground
-          storage_component['volume'] = random.uniform(0, max_available_space)
-          if storage_component['heating_coil_capacity'] is not None:
-            if cte.HEATING in energy_system.demand_types:
-              storage_component['heating_coil_capacity'] = random.uniform(0, max(
-                design_period_energy_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES)
-            else:
-              storage_component['heating_coil_capacity'] = random.uniform(0, max(
-                design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES)
-    return individual
-
-  def solve_ga(self, building, energy_system):
-    self.initialize_population(building, energy_system)
-    for individual in self.population:
-      individual.score_evaluation()
-    pareto_population = []
-
-    for generation in range(1, self.generations + 1):
-      print(f"Generation {generation}")
-      fronts = self.fast_non_dominated_sort()
-
-      # Ensure the front calculation is valid
-      if not fronts or not fronts[0]:
-        print("Warning: No valid non-dominated front found.")
-        continue
-
-      # Calculate crowding distances and select individuals
-      crowding_distances = [self.calculate_crowding_distance(front) for front in fronts]
-      self.population = self.nsga2_selection(self.population, fronts, crowding_distances)
-
-      # Add only valid indices to pareto_population
-      pareto_population.extend([self.population[i] for i in fronts[0] if i < len(self.population)])
-
-      # Check population bounds
-      if len(pareto_population) > self.population_size:
-        pareto_population = pareto_population[:self.population_size]
-
-      # Generate the next population through crossover and mutation
-      next_population = []
-      while len(next_population) < self.population_size:
-        parent1, parent2 = random.choice(self.population), random.choice(self.population)
-        child1, child2 = self.crossover(parent1, parent2)
-        self.mutate(child1.individual, building, energy_system)
-        self.mutate(child2.individual, building, energy_system)
-        child1.score_evaluation()
-        child2.score_evaluation()
-        next_population.extend([child1, child2])
-
-      self.population = next_population[:self.population_size]
-
-    # Ensure pareto_population contains the correct non-dominated individuals before TOPSIS
-    if not pareto_population:
-      print("No Pareto solutions found during optimization.")
-      return None
-
-    # Recalculate pareto front indices based on updated pareto_population
-    fronts = self.fast_non_dominated_sort()
-    pareto_front_indices = fronts[0] if fronts else []
-    pareto_front_indices = [i for i in pareto_front_indices if i < len(pareto_population)]
-    print(f"Final pareto_front_indices: {pareto_front_indices}, pareto_population size: {len(pareto_population)}")
-
-    if not pareto_front_indices:
-      print("No valid solution found after TOPSIS due to empty pareto front indices.")
-      return None
-
-    global_pareto_front = [pareto_population[i] for i in pareto_front_indices]
-
-    # Get the top N solutions with TOPSIS
-    top_n = 3  # Adjust this value based on how many top solutions you want to explore
-    self.best_solution = self.topsis_decision_making(global_pareto_front, top_n=top_n)
-
-    # Print the top N solutions
-    if self.best_solution:
-      print("Top solutions after TOPSIS:")
-      for idx, solution in enumerate(self.best_solution, 1):
-        print(f"Solution {idx}: LCC = {solution.individual['lcc']}, "
-              f"LCE = {solution.individual['total_energy_consumption']}")
-    else:
-      print("No valid solutions found after TOPSIS.")
-
-    if pareto_population:
-      self.plot_pareto_front(pareto_population)
-
-    return self.best_solution
-
-  @staticmethod
-  def design_period_identification(building):
-    def get_start_end_indices(max_day_index, total_days):
-      if max_day_index > 0 and max_day_index < total_days - 1:
-        start_index = (max_day_index - 1) * 24
-        end_index = (max_day_index + 2) * 24
-      elif max_day_index == 0:
-        start_index = 0
-        end_index = (max_day_index + 2) * 24
-      else:
-        start_index = (max_day_index - 1) * 24
-        end_index = total_days * 24
-      return start_index, end_index
-
-    # Calculate daily demands
-    heating_daily_demands = [sum(building.heating_demand[cte.HOUR][i:i + 24]) for i in
-                             range(0, len(building.heating_demand[cte.HOUR]), 24)]
-    cooling_daily_demands = [sum(building.cooling_demand[cte.HOUR][i:i + 24]) for i in
-                             range(0, len(building.cooling_demand[cte.HOUR]), 24)]
-    dhw_daily_demands = [sum(building.domestic_hot_water_heat_demand[cte.HOUR][i:i + 24]) for i in
-                         range(0, len(building.domestic_hot_water_heat_demand[cte.HOUR]), 24)]
-    # Get the day with maximum demand for each type
-    heating_max_day = heating_daily_demands.index(max(heating_daily_demands))
-    cooling_max_day = cooling_daily_demands.index(max(cooling_daily_demands))
-    dhw_max_day = dhw_daily_demands.index(max(dhw_daily_demands))
-    # Get the start and end indices for each demand type
-    heating_start, heating_end = get_start_end_indices(heating_max_day, len(heating_daily_demands))
-    cooling_start, cooling_end = get_start_end_indices(cooling_max_day, len(cooling_daily_demands))
-    dhw_start, dhw_end = get_start_end_indices(dhw_max_day, len(dhw_daily_demands))
-    # Return the design period energy demands
-    return {
-      f'{cte.HEATING}': {'demands': building.heating_demand[cte.HOUR][heating_start:heating_end],
-                         'start_index': heating_start, 'end_index': heating_end},
-      f'{cte.COOLING}': {'demands': building.cooling_demand[cte.HOUR][cooling_start:cooling_end],
-                         'start_index': cooling_start, 'end_index': cooling_end},
-      f'{cte.DOMESTIC_HOT_WATER}': {'demands': building.domestic_hot_water_heat_demand[cte.HOUR][dhw_start:dhw_end],
-                                    'start_index': dhw_start, 'end_index': dhw_end}
-    }
-
-  def topsis_decision_making(self, pareto_front, top_n=5):
-    """
-    Perform TOPSIS decision-making to choose the best solutions from the Pareto front.
-
-    :param pareto_front: List of individuals in the Pareto front
-    :param top_n: Number of top solutions to select based on TOPSIS ranking
-    :return: List of top N individuals based on TOPSIS ranking
-    """
-    # Filter out infinite values from the pareto front before processing
-    pareto_front = [ind for ind in pareto_front if all(math.isfinite(v) for v in ind.individual['fitness_score'])]
-    if not pareto_front:
-      return None
-
-    # Step 1: Normalize the objective functions (cost and energy consumption)
-    min_lcc = min([ind.individual['lcc'] for ind in pareto_front])
-    max_lcc = max([ind.individual['lcc'] for ind in pareto_front])
-    min_lce = min([ind.individual['total_energy_consumption'] for ind in pareto_front])
-    max_lce = max([ind.individual['total_energy_consumption'] for ind in pareto_front])
-
-    normalized_pareto_front = []
-    for ind in pareto_front:
-      normalized_lcc = (ind.individual['lcc'] - min_lcc) / (max_lcc - min_lcc) if max_lcc > min_lcc else 0
-      normalized_lce = (ind.individual['total_energy_consumption'] - min_lce) / (
-          max_lce - min_lce) if max_lce > min_lce else 0
-      normalized_pareto_front.append((ind, normalized_lcc, normalized_lce))
-
-    # Step 2: Calculate the ideal and worst solutions
-    ideal_solution = (0, 0)  # Ideal is minimum LCC and minimum LCE (0, 0 after normalization)
-    worst_solution = (1, 1)  # Worst is maximum LCC and maximum LCE (1, 1 after normalization)
-
-    # Step 3: Calculate the distance to the ideal and worst solutions
-    best_distances = []
-    worst_distances = []
-    for ind, normalized_lcc, normalized_lce in normalized_pareto_front:
-      distance_to_ideal = math.sqrt(
-        (normalized_lcc - ideal_solution[0]) ** 2 + (normalized_lce - ideal_solution[1]) ** 2)
-      distance_to_worst = math.sqrt(
-        (normalized_lcc - worst_solution[0]) ** 2 + (normalized_lce - worst_solution[1]) ** 2)
-      best_distances.append(distance_to_ideal)
-      worst_distances.append(distance_to_worst)
-
-    # Step 4: Calculate relative closeness to the ideal solution
-    similarity = [worst / (best + worst) for best, worst in zip(best_distances, worst_distances)]
-
-    # Step 5: Select the top N individuals with the highest similarity scores
-    top_indices = sorted(range(len(similarity)), key=lambda i: similarity[i], reverse=True)[:top_n]
-    top_solutions = [pareto_front[i] for i in top_indices]
-
-    # Plot the similarity scores
-    self.plot_topsis_similarity(similarity)
-
-    return top_solutions
-
-  @staticmethod
-  def plot_topsis_similarity(similarity):
-    """
-    Plot the TOPSIS similarity scores for visualization.
-
-    :param similarity: List of similarity scores for each individual in the Pareto front
-    """
-    plt.figure(figsize=(10, 6))
-    plt.plot(range(len(similarity)), similarity, 'bo-', label='TOPSIS Similarity Scores')
-    plt.xlabel('Pareto Front Solution Index')
-    plt.ylabel('Similarity Score')
-    plt.title('TOPSIS Similarity Scores for Pareto Front Solutions')
-    plt.legend()
-    plt.grid(True)
-    plt.show()
-
-  @staticmethod
-  def plot_pareto_front(pareto_population):
-    # Extract LCC and LCE for plotting
-    lcc_values = [individual.individual['lcc'] for individual in pareto_population]
-    lce_values = [individual.individual['total_energy_consumption'] for individual in pareto_population]
-    plt.figure(figsize=(10, 6))
-    plt.scatter(lcc_values, lce_values, color='blue', label='Pareto Front', alpha=0.6, edgecolors='w', s=80)
-    plt.title('Pareto Front for Life Cycle Cost vs Life Cycle Energy')
-    plt.xlabel('Life Cycle Cost (LCC)')
-    plt.ylabel('Life Cycle Energy (LCE)')
-    plt.grid(True)
-    plt.legend()
-    plt.show()

energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/genetic_algorithm/multi_objective_genetic_algorithm_rethinking.py

@@ -1,94 +0,0 @@
-import copy
-import math
-import random
-import hub.helpers.constants as cte
-from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.genetic_algorithm.individual import \
-  Individual
-import matplotlib.pyplot as plt
-import time
-
-
-class MultiObjectiveGeneticAlgorithm:
-  def __init__(self, population_size=100, generations=50, crossover_rate=0.8, mutation_rate=0.1,
-               optimization_scenario=None, output_path=None):
-    self.population_size = population_size
-    self.population = []
-    self.generations = generations
-    self.crossover_rate = crossover_rate
-    self.mutation_rate = mutation_rate
-    self.optimization_scenario = optimization_scenario
-    self.list_of_solutions = []
-    self.best_solution = None
-    self.best_solution_generation = None
-    self.output_path = output_path
-
-# Initialize Population
-  def initialize_population(self, building, energy_system):
-    design_period_start_time = time.time()
-    design_period_energy_demands = self.design_period_identification(building)
-    design_period_time = time.time() - design_period_start_time
-    print(f"design period identification took {design_period_time:.2f} seconds")
-    initializing_time_start = time.time()
-    attempts = 0
-    max_attempts = self.population_size * 20
-    while len(self.population) < self.population_size and attempts < max_attempts:
-      individual = Individual(building=building,
-                              energy_system=energy_system,
-                              design_period_energy_demands=design_period_energy_demands,
-                              optimization_scenario=self.optimization_scenario)
-      individual.initialization()
-      individual.score_evaluation()
-      attempts += 1
-      if individual.feasibility:
-        self.population.append(individual)
-    if len(self.population) < self.population_size:
-      raise RuntimeError(f"Could not generate a feasible population of size {self.population_size}. "
-                         f"Only {len(self.population)} feasible individuals were generated.")
-    initializing_time = time.time() - initializing_time_start
-    print(f"initializing took {initializing_time:.2f} seconds")
-
-
-  @staticmethod
-  def design_period_identification(building):
-    def get_start_end_indices(max_day_index, total_days):
-      if max_day_index > 0 and max_day_index < total_days - 1:
-        start_index = (max_day_index - 1) * 24
-        end_index = (max_day_index + 2) * 24
-      elif max_day_index == 0:
-        start_index = 0
-        end_index = (max_day_index + 2) * 24
-      else:
-        start_index = (max_day_index - 1) * 24
-        end_index = total_days * 24
-      return start_index, end_index
-
-    # Calculate daily demands
-    heating_daily_demands = [sum(building.heating_demand[cte.HOUR][i:i + 24]) for i in
-                             range(0, len(building.heating_demand[cte.HOUR]), 24)]
-    cooling_daily_demands = [sum(building.cooling_demand[cte.HOUR][i:i + 24]) for i in
-                             range(0, len(building.cooling_demand[cte.HOUR]), 24)]
-    dhw_daily_demands = [sum(building.domestic_hot_water_heat_demand[cte.HOUR][i:i + 24]) for i in
-                         range(0, len(building.domestic_hot_water_heat_demand[cte.HOUR]), 24)]
-    # Get the day with maximum demand for each type
-    heating_max_day = heating_daily_demands.index(max(heating_daily_demands))
-    cooling_max_day = cooling_daily_demands.index(max(cooling_daily_demands))
-    dhw_max_day = dhw_daily_demands.index(max(dhw_daily_demands))
-    # Get the start and end indices for each demand type
-    heating_start, heating_end = get_start_end_indices(heating_max_day, len(heating_daily_demands))
-    cooling_start, cooling_end = get_start_end_indices(cooling_max_day, len(cooling_daily_demands))
-    dhw_start, dhw_end = get_start_end_indices(dhw_max_day, len(dhw_daily_demands))
-    # Return the design period energy demands
-    return {
-      f'{cte.HEATING}': {'demands': building.heating_demand[cte.HOUR][heating_start:heating_end],
-                         'start_index': heating_start, 'end_index': heating_end},
-      f'{cte.COOLING}': {'demands': building.cooling_demand[cte.HOUR][cooling_start:cooling_end],
-                         'start_index': cooling_start, 'end_index': cooling_end},
-      f'{cte.DOMESTIC_HOT_WATER}': {'demands': building.domestic_hot_water_heat_demand[cte.HOUR][dhw_start:dhw_end],
-                                    'start_index': dhw_start, 'end_index': dhw_end}
-    }
-
-  def solve_ga(self, building, energy_system):
-    self.initialize_population(building, energy_system)
-    for individual in self.population:
-      print(individual.individual)
-    print([ind.fitness_score for ind in self.population])

energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/genetic_algorithm/single_objective_genetic_algorithm.py

@@ -1,342 +0,0 @@
-import copy
-import math
-import random
-import hub.helpers.constants as cte
-from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.genetic_algorithm.individual import \
-  Individual
-
-
-class SingleObjectiveGeneticAlgorithm:
-  def __init__(self, population_size=100, generations=20, crossover_rate=0.8, mutation_rate=0.1,
-               optimization_scenario=None, output_path=None):
-    self.population_size = population_size
-    self.population = []
-    self.generations = generations
-    self.crossover_rate = crossover_rate
-    self.mutation_rate = mutation_rate
-    self.optimization_scenario = optimization_scenario
-    self.list_of_solutions = []
-    self.best_solution = None
-    self.best_solution_generation = None
-    self.output_path = output_path
-
-  # Initialize Population
-  def initialize_population(self, building, energy_system):
-    """
-    Initialize a population of individuals with feasible configurations for optimizing the sizes of
-    generation and storage components of an energy system.
-
-    :param building: Building object with associated data
-    :param energy_system: Energy system to optimize
-    """
-    design_period_energy_demands = self.design_period_identification(building)
-    attempts = 0  # Track attempts to avoid an infinite loop in rare cases
-    max_attempts = self.population_size * 5
-
-    while len(self.population) < self.population_size and attempts < max_attempts:
-      individual = Individual(building=building,
-                              energy_system=energy_system,
-                              design_period_energy_demands=design_period_energy_demands,
-                              optimization_scenario=self.optimization_scenario)
-
-      individual.initialization()
-      attempts += 1
-
-    # Enhanced feasibility check
-      if self.initial_population_feasibility_check(individual, energy_system.demand_types, design_period_energy_demands):
-        self.population.append(individual)
-
-    # Raise an error or print a warning if the population size goal is not met after max_attempts
-    if len(self.population) < self.population_size:
-      raise RuntimeError(f"Could not generate a feasible population of size {self.population_size}. "
-                         f"Only {len(self.population)} feasible individuals were generated.")
-
-  @staticmethod
-  def initial_population_feasibility_check(individual, demand_types, design_period_demands):
-    """
-    Check if the individual meets basic feasibility requirements for heating, cooling, and DHW capacities
-    and storage volume.
-
-    :param individual: Individual to check
-    :param demand_types: List of demand types (e.g., heating, cooling, DHW)
-    :param design_period_demands: Design period demand values for heating, cooling, and DHW
-    :return: True if feasible, False otherwise
-    """
-    # Calculate total heating and cooling capacities
-    total_heating_capacity = sum(
-      component['heating_capacity'] for component in individual.individual['Generation Components']
-      if component['heating_capacity'] is not None
-    )
-    total_cooling_capacity = sum(
-      component['cooling_capacity'] for component in individual.individual['Generation Components']
-      if component['cooling_capacity'] is not None
-    )
-
-    # Maximum demands for each demand type (converted to kW)
-    max_heating_demand = max(design_period_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES
-    max_cooling_demand = max(design_period_demands[cte.COOLING]['demands']) / cte.WATTS_HOUR_TO_JULES
-    max_dhw_demand = max(design_period_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES
-
-    # Check heating capacity feasibility
-    if cte.HEATING in demand_types and total_heating_capacity < 0.5 * max_heating_demand:
-      return False
-
-    # Check DHW capacity feasibility
-    if cte.DOMESTIC_HOT_WATER in demand_types and total_heating_capacity < 0.5 * max_dhw_demand:
-      return False
-
-    # Check cooling capacity feasibility
-    if cte.COOLING in demand_types and total_cooling_capacity < 0.5 * max_cooling_demand:
-      return False
-
-    # Check storage volume feasibility
-    total_volume = sum(
-      component['volume'] for component in individual.individual['Energy Storage Components']
-      if component['volume'] is not None
-    )
-    # Limit storage to 10% of building's available space
-    max_storage_volume = individual.available_space * 0.1
-    if total_volume > max_storage_volume:
-      return False
-
-    return True  # Feasible if all checks are passed
-
-  def order_population(self):
-    """
-    ordering the population based on the fitness score in ascending order
-    :return:
-    """
-    self.population = sorted(self.population, key=lambda x: x.fitness_score)
-
-  def tournament_selection(self):
-    selected = []
-    for _ in range(len(self.population)):
-      i, j = random.sample(range(self.population_size), 2)
-      if self.population[i].individual['fitness_score'] < self.population[j].individual['fitness_score']:
-        selected.append(copy.deepcopy(self.population[i]))
-      else:
-        selected.append(copy.deepcopy(self.population[j]))
-    return selected
-
-  def crossover(self, parent1, parent2):
-    """
-    Crossover between two parents to produce two children.
-
-    swaps generation components and storage components between the two parents with a 50% chance.
-
-    :param parent1: First parent individual.
-    :param parent2: second parent individual.
-    :return: Two child individuals (child1 and child2).
-    """
-    if random.random() < self.crossover_rate:
-        # Deep copy of the parents to create children
-      child1, child2 = copy.deepcopy(parent1), copy.deepcopy(parent2)
-    # Crossover for Generation Components
-      for i in range(len(parent1.individual['Generation Components'])):
-        if random.random() < 0.5:
-            # swap the entire generation component
-          child1.individual['Generation Components'][i], child2.individual['Generation Components'][i] = (
-            child2.individual['Generation Components'][i],
-            child1.individual['Generation Components'][i]
-          )
-
-    # Crossover for Energy storage Components
-      for i in range(len(parent1.individual['Energy Storage Components'])):
-        if random.random() < 0.5:
-            # swap the entire storage component
-          child1.individual['Energy Storage Components'][i], child2.individual['Energy Storage Components'][i] = (
-            child2.individual['Energy Storage Components'][i],
-            child1.individual['Energy Storage Components'][i]
-          )
-
-      return child1, child2
-    else:
-        # If crossover does not happen, return copies of the original parents
-      return copy.deepcopy(parent1), copy.deepcopy(parent2)
-
-  def mutate(self, individual, building, energy_system):
-    """
-    Mutates the individual's generation and storage components.
-
-    - `individual`: The individual to mutate (contains generation and storage components).
-    - `building`: Building data that contains constraints such as peak heating load and available space.
-
-    Returns the mutated individual.
-    """
-    design_period_energy_demands = self.design_period_identification(building)
-    # Mutate Generation Components
-    for generation_component in individual['Generation Components']:
-      if random.random() < self.mutation_rate:
-        if (generation_component['nominal_heating_efficiency'] is not None and cte.HEATING or cte.DOMESTIC_HOT_WATER in
-                energy_system.demand_types):
-            # Mutate heating capacity
-          if cte.HEATING in energy_system.demand_types:
-            generation_component['heating_capacity'] = random.uniform(
-              0, max(design_period_energy_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES)
-          else:
-            generation_component['heating_capacity'] = random.uniform(
-              0, max(design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES)
-        if generation_component['nominal_cooling_efficiency'] is not None and cte.COOLING in energy_system.demand_types:
-            # Mutate cooling capacity
-          generation_component['cooling_capacity'] = random.uniform(
-            0, max(design_period_energy_demands[cte.COOLING]['demands']) / cte.WATTS_HOUR_TO_JULES)
-    # Mutate storage Components
-    for storage_component in individual['Energy Storage Components']:
-      if random.random() < self.mutation_rate:
-        if storage_component['type'] == f'{cte.THERMAL}_storage':
-            # Mutate the volume of thermal storage
-          max_available_space = 0.01 * building.volume / building.storeys_above_ground
-          storage_component['volume'] = random.uniform(0, max_available_space)
-          if storage_component['heating_coil_capacity'] is not None:
-            if cte.HEATING in energy_system.demand_types:
-              storage_component['heating_coil_capacity'] = random.uniform(0, max(
-                design_period_energy_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES)
-            else:
-              storage_component['heating_coil_capacity'] = random.uniform(0, max(
-                design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES)
-    return individual
-
-  def solve_ga(self, building, energy_system):
-    """
-    solving GA for a single energy system. Here are the steps:
-    1. Initialize population using the "initialize_population" method in this class.
-    2. Evaluate the initial population using the "score_evaluation" method in the Individual class.
-    3. sort population based on fitness score.
-    4. Repeat selection, crossover, and mutation for a fixed number of generations.
-    5. Track the best solution found during the optimization process.
-
-    :param building: Building object for the energy system.
-    :param energy_system: Energy system to optimize.
-    :return: Best solution after running the GA.
-    """
-    # step 1: Initialize the population
-    self.initialize_population(building, energy_system)
-    # step 2: Evaluate the initial population
-    for individual in self.population:
-      individual.score_evaluation()
-    # step 3: Order population based on fitness scores
-    self.order_population()
-    print([individual.fitness_score for individual in self.population])
-    # Track the best solution
-    self.best_solution = self.population[0]
-    self.best_solution_generation = 0
-    self.list_of_solutions.append(copy.deepcopy(self.best_solution.individual))
-    # step 4: Run GA for a fixed number of generations
-    for generation in range(1, self.generations):
-      print(f"Generation {generation}")
-    # selection (using tournament selection)
-      selected_population = self.tournament_selection()
-    # Create the next generation through crossover and mutation
-      next_population = []
-      for i in range(0, self.population_size, 2):
-        parent1 = selected_population[i]
-        parent2 = selected_population[i + 1] if (i + 1) < len(selected_population) else selected_population[0]
-    # step 5: Apply crossover
-        child1, child2 = self.crossover(parent1, parent2)
-    # step 6: Apply mutation
-        self.mutate(child1.individual, building, energy_system)
-        self.mutate(child2.individual, building, energy_system)
-    # step 7: Evaluate the children
-        child1.score_evaluation()
-        child2.score_evaluation()
-        next_population.extend([child1, child2])
-    # Replace old population with the new one
-      self.population = next_population
-    # step 8: sort the new population based on fitness
-      self.order_population()
-      print([individual.fitness_score for individual in self.population])
-    # Track the best solution found in this generation
-      if self.population[0].individual['fitness_score'] < self.best_solution.individual['fitness_score']:
-        self.best_solution = self.population[0]
-        self.best_solution_generation = generation
-    # store the best solution in the list of solutions
-      self.list_of_solutions.append(copy.deepcopy(self.population[0].individual))
-    print(f"Best solution found in generation {self.best_solution_generation}")
-    print(f"Best solution: {self.best_solution.individual}")
-    return self.best_solution
-
-  @staticmethod
-  def topsis_decision_making(pareto_front):
-    """
-    Perform TOPSIS decision-making to choose the best solution from the Pareto front.
-
-    :param pareto_front: List of individuals in the Pareto front
-    :return: The best individual based on TOPSIS ranking
-    """
-    # Step 1: Normalize the objective functions (cost and energy consumption)
-    min_lcc = min([ind.individual['lcc'] for ind in pareto_front])
-    max_lcc = max([ind.individual['lcc'] for ind in pareto_front])
-    min_lce = min([ind.individual['total_energy_consumption'] for ind in pareto_front])
-    max_lce = max([ind.individual['total_energy_consumption'] for ind in pareto_front])
-
-    normalized_pareto_front = []
-    for ind in pareto_front:
-      normalized_lcc = (ind.individual['lcc'] - min_lcc) / (max_lcc - min_lcc) if max_lcc > min_lcc else 0
-      normalized_lce = (ind.individual['total_energy_consumption'] - min_lce) / (
-            max_lce - min_lce) if max_lce > min_lce else 0
-      normalized_pareto_front.append((ind, normalized_lcc, normalized_lce))
-
-    # Step 2: Calculate the ideal and worst solutions
-    ideal_solution = (0, 0)  # Ideal is minimum LCC and minimum LCE (0, 0 after normalization)
-    worst_solution = (1, 1)  # Worst is maximum LCC and maximum LCE (1, 1 after normalization)
-
-    # Step 3: Calculate the distance to the ideal and worst solutions
-    best_distances = []
-    worst_distances = []
-
-    for ind, normalized_lcc, normalized_lce in normalized_pareto_front:
-      distance_to_ideal = math.sqrt(
-        (normalized_lcc - ideal_solution[0]) ** 2 + (normalized_lce - ideal_solution[1]) ** 2)
-      distance_to_worst = math.sqrt(
-        (normalized_lcc - worst_solution[0]) ** 2 + (normalized_lce - worst_solution[1]) ** 2)
-      best_distances.append(distance_to_ideal)
-      worst_distances.append(distance_to_worst)
-
-    # Step 4: Calculate relative closeness to the ideal solution
-    similarity = [worst / (best + worst) for best, worst in zip(best_distances, worst_distances)]
-
-    # Step 5: Select the individual with the highest similarity score
-    best_index = similarity.index(max(similarity))
-    best_solution = pareto_front[best_index]
-
-    return best_solution
-
-  @staticmethod
-  def design_period_identification(building):
-    def get_start_end_indices(max_day_index, total_days):
-      if max_day_index > 0 and max_day_index < total_days - 1:
-        start_index = (max_day_index - 1) * 24
-        end_index = (max_day_index + 2) * 24
-      elif max_day_index == 0:
-        start_index = 0
-        end_index = (max_day_index + 2) * 24
-      else:
-        start_index = (max_day_index - 1) * 24
-        end_index = total_days * 24
-      return start_index, end_index
-
-    # Calculate daily demands
-    heating_daily_demands = [sum(building.heating_demand[cte.HOUR][i:i + 24]) for i in
-                             range(0, len(building.heating_demand[cte.HOUR]), 24)]
-    cooling_daily_demands = [sum(building.cooling_demand[cte.HOUR][i:i + 24]) for i in
-                             range(0, len(building.cooling_demand[cte.HOUR]), 24)]
-    dhw_daily_demands = [sum(building.domestic_hot_water_heat_demand[cte.HOUR][i:i + 24]) for i in
-                         range(0, len(building.domestic_hot_water_heat_demand[cte.HOUR]), 24)]
-    # Get the day with maximum demand for each type
-    heating_max_day = heating_daily_demands.index(max(heating_daily_demands))
-    cooling_max_day = cooling_daily_demands.index(max(cooling_daily_demands))
-    dhw_max_day = dhw_daily_demands.index(max(dhw_daily_demands))
-    # Get the start and end indices for each demand type
-    heating_start, heating_end = get_start_end_indices(heating_max_day, len(heating_daily_demands))
-    cooling_start, cooling_end = get_start_end_indices(cooling_max_day, len(cooling_daily_demands))
-    dhw_start, dhw_end = get_start_end_indices(dhw_max_day, len(dhw_daily_demands))
-    # Return the design period energy demands
-    return {
-      f'{cte.HEATING}': {'demands': building.heating_demand[cte.HOUR][heating_start:heating_end],
-                         'start_index': heating_start, 'end_index': heating_end},
-      f'{cte.COOLING}': {'demands': building.cooling_demand[cte.HOUR][cooling_start:cooling_end],
-                         'start_index': cooling_start, 'end_index': cooling_end},
-      f'{cte.DOMESTIC_HOT_WATER}': {'demands': building.domestic_hot_water_heat_demand[cte.HOUR][dhw_start:dhw_end],
-                                    'start_index': dhw_start, 'end_index': dhw_end}
-    }
-

energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/optimal_sizing.py

@@ -1,42 +0,0 @@
-import hub.helpers.constants as cte
-from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.genetic_algorithm.single_objective_genetic_algorithm import \
-  SingleObjectiveGeneticAlgorithm
-
-
-class OptimalSizing:
-  def __init__(self, city, optimization_scenario):
-    self.city = city
-    self.optimization_scenario = optimization_scenario
-
-  def enrich_buildings(self):
-    for building in self.city.buildings:
-      for energy_system in building.energy_systems:
-        if len(energy_system.generation_systems) == 1 and energy_system.generation_systems[0].energy_storage_systems is None:
-          if energy_system.generation_systems[0].system_type == cte.PHOTOVOLTAIC:
-            pass
-          else:
-            if cte.HEATING in energy_system.demand_types:
-              if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
-                design_load = max([building.heating_demand[cte.HOUR][i] +
-                                   building.domestic_hot_water_heat_demand[cte.HOUR][i] for i in
-                                   range(len(building.heating_demand))]) / cte.WATTS_HOUR_TO_JULES
-              else:
-                design_load = building.heating_peak_load[cte.YEAR][0]
-              energy_system.generation_systems[0].nominal_heat_output = design_load
-            elif cte.COOLING in energy_system.demand_types:
-              energy_system.generation_systems[0].nominal_cooling_output = building.cooling_peak_load[cte.YEAR][0]
-        else:
-          optimized_system = SingleObjectiveGeneticAlgorithm(optimization_scenario=self.optimization_scenario).solve_ga(building, energy_system)
-          for generation_system in energy_system.generation_systems:
-            system_type = generation_system.system_type
-            for generation_component in optimized_system.individual['Generation Components']:
-              if generation_component['type'] == system_type:
-                generation_system.nominal_heat_output = generation_component['heating_capacity']
-                generation_system.nominal_cooling_output = generation_component['cooling_capacity']
-            if generation_system.energy_storage_systems is not None:
-              for storage_system in generation_system.energy_storage_systems:
-                storage_type = f'{storage_system.type_energy_stored}_storage'
-                for storage_component in optimized_system.individual['Energy Storage Components']:
-                  if storage_component['type'] == storage_type:
-                    storage_system.nominal_capacity = storage_component['capacity']
-                    storage_system.volume = storage_component['volume']

energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/peak_load_sizing.py

@@ -1,99 +0,0 @@
-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

@@ -1,595 +0,0 @@
-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.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', 'Central 4 Pipes Air to Water Heat Pump and Gas Boiler with Independent Water Heating and PV']:
-        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] / 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

@@ -1,176 +0,0 @@
-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

@@ -1,123 +0,0 @@
-"""
-This project aims to assign energy systems archetype names to Montreal buildings.
-The random assignation is based on statistical information extracted from different sources, being:
-- For residential buildings:
-  - SHEU 2015: https://oee.nrcan.gc.ca/corporate/statistics/neud/dpa/menus/sheu/2015/tables.cfm
-- For non-residential buildings:
-  - Montreal dataportal: https://dataportalforcities.org/north-america/canada/quebec/montreal
-  - https://www.eia.gov/consumption/commercial/data/2018/
-"""
-import json
-import random
-
-from hub.city_model_structure.building import Building
-
-energy_systems_format = 'montreal_custom'
-
-# parameters:
-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,
-                                  'system 3 and 4 electricity': 0,
-                                  'system 5 gas': 0,
-                                  'system 5 electricity': 0,
-                                  'system 6 gas': 0,
-                                  'system 6 electricity': 0,
-                                  'system 8 gas': 15,
-                                  'system 8 electricity': 35}
-
-residential_new_systems_percentage = {
-    'Central Hydronic Air and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and PV': 100,
-    'Central Hydronic Air and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW and PV': 0,
-    'Central Hydronic Ground and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and PV': 0,
-    'Central Hydronic Ground and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW '
-    'and PV': 0,
-    'Central Hydronic Water and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and PV': 0,
-    'Central Hydronic Water and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW '
-    'and 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,
-    'Rooftop PV System': 0
-}
-
-non_residential_systems_percentage = {'system 1 gas': 0,
-                                      'system 1 electricity': 0,
-                                      'system 2 gas': 0,
-                                      'system 2 electricity': 0,
-                                      'system 3 and 4 gas': 39,
-                                      'system 3 and 4 electricity': 36,
-                                      'system 5 gas': 0,
-                                      'system 5 electricity': 0,
-                                      'system 6 gas': 13,
-                                      'system 6 electricity': 12,
-                                      'system 8 gas': 0,
-                                      'system 8 electricity': 0}
-
-
-def _retrieve_buildings(path, year_of_construction_field=None,
-                        function_field=None, function_to_hub=None, aliases_field=None):
-  _buildings = []
-  with open(path, 'r', encoding='utf8') as json_file:
-    _geojson = json.loads(json_file.read())
-    for feature in _geojson['features']:
-      _building = {}
-      year_of_construction = None
-      if year_of_construction_field is not None:
-        year_of_construction = int(feature['properties'][year_of_construction_field])
-      function = None
-      if function_field is not None:
-        function = feature['properties'][function_field]
-        if function_to_hub is not None:
-          # use the transformation dictionary to retrieve the proper function
-          if function in function_to_hub:
-            function = function_to_hub[function]
-      building_name = ''
-      building_aliases = []
-      if 'id' in feature:
-        building_name = feature['id']
-      if aliases_field is not None:
-        for alias_field in aliases_field:
-          building_aliases.append(feature['properties'][alias_field])
-      _building['year_of_construction'] = year_of_construction
-      _building['function'] = function
-      _building['building_name'] = building_name
-      _building['building_aliases'] = building_aliases
-      _buildings.append(_building)
-  return _buildings
-
-
-def call_random(_buildings: [Building], _systems_percentage):
-  _buildings_with_systems = []
-  _systems_distribution = []
-  _selected_buildings = list(range(0, len(_buildings)))
-  random.shuffle(_selected_buildings)
-  total = 0
-  maximum = 0
-  add_to = 0
-  for _system in _systems_percentage:
-    if _systems_percentage[_system] > 0:
-      number_of_buildings = round(_systems_percentage[_system] / 100 * len(_selected_buildings))
-      _systems_distribution.append({'system': _system, 'number': _systems_percentage[_system],
-                                    'number_of_buildings': number_of_buildings})
-      if number_of_buildings > maximum:
-        maximum = number_of_buildings
-        add_to = len(_systems_distribution) - 1
-      total += number_of_buildings
-  missing = 0
-  if total != len(_selected_buildings):
-    missing = len(_selected_buildings) - total
-  if missing != 0:
-    _systems_distribution[add_to]['number_of_buildings'] += missing
-  _position = 0
-  for case in _systems_distribution:
-    for i in range(0, case['number_of_buildings']):
-      _buildings[_selected_buildings[_position]].energy_systems_archetype_name = case['system']
-      _position += 1
-  return _buildings
-

energy_system_modelling_package/report_creation.py

@@ -1,119 +0,0 @@
-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)])
-

hub/catalog_factories/construction/cerc_catalog.py

@@ -126,6 +126,12 @@ class CercCatalog(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']:
@@ -163,7 +169,9 @@ class CercCatalog(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/eilat_catalog.py

@@ -126,6 +126,10 @@ class EilatCatalog(Catalog):
                                                        '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']:
@@ -164,8 +168,8 @@ class EilatCatalog(Catalog):
                                            None,
                                            infiltration_rate_for_ventilation_system_off,
                                            infiltration_rate_for_ventilation_system_on,
-                                           0,
+                                           infiltration_rate_area_for_ventilation_system_off,
-                                           0))
+                                           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']:
@@ -163,8 +169,8 @@ class NrelCatalog(Catalog):
                                            indirect_heated_ratio,
                                            infiltration_rate_for_ventilation_system_off,
                                            infiltration_rate_for_ventilation_system_on,
-                                           0,
+                                           infiltration_rate_area_for_ventilation_system_off,
-                                           0))
+                                           infiltration_rate_area_for_ventilation_system_on))
     return _catalog_archetypes
 
   def names(self, category=None):

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):
     """
@@ -43,6 +52,7 @@ class Archetype:
       _systems.append(_system.to_dictionary())
     content = {
       'Archetype': {
+        'cluster_id': self.cluster_id,
         'name': self.name,
         'systems': _systems
       }

hub/catalog_factories/data_models/energy_systems/generation_system.py

@@ -20,7 +20,7 @@ class GenerationSystem(ABC):
   """
 
   def __init__(self, system_id, name, model_name=None, manufacturer=None, fuel_type=None,
-               distribution_systems=None, energy_storage_systems=None):
+               distribution_systems=None, energy_storage_systems=None, number_of_units=None):
     self._system_id = system_id
     self._name = name
     self._model_name = model_name
@@ -28,6 +28,7 @@ class GenerationSystem(ABC):
     self._fuel_type = fuel_type
     self._distribution_systems = distribution_systems
     self._energy_storage_systems = energy_storage_systems
+    self._number_of_units = number_of_units
 
   @property
   def id(self):

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,
+               standard_test_condition_maximum_power=None, standard_test_condition_radiation=None,
-               distribution_systems=None, energy_storage_systems=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/energy_systems/montreal_future_system_catalogue.py

@@ -193,6 +193,7 @@ 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 +216,7 @@ 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,
@@ -379,6 +381,7 @@ 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 +389,7 @@ 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:
@@ -880,8 +880,8 @@ class Building(CityObject):
             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:
+                if storage_system.type_energy_stored == 'thermal' and storage_system.heating_coil_capacity is not None:
-                  fuel_breakdown[cte.ELECTRICITY][f'{demand_type}'] += storage_system.heating_coil_energy_consumption[cte.YEAR][0]
+                  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 = []
@@ -913,3 +913,19 @@ class Building(CityObject):
     self._fuel_consumption_breakdown = fuel_breakdown
     return self._fuel_consumption_breakdown
 
+  @property
+  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
+

hub/city_model_structure/building_demand/surface.py

@@ -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):
     """

hub/city_model_structure/building_demand/thermal_archetype.py

@@ -141,7 +141,7 @@ class ThermalArchetype:
     Get infiltration rate for ventilation system off in l/s/m2
     :return: float
     """
-    return self._infiltration_rate_for_ventilation_system_off
+    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):
@@ -149,7 +149,7 @@ class ThermalArchetype:
     Set infiltration rate for ventilation system off in l/s/m2
     :param value: float
     """
-    self._infiltration_rate_for_ventilation_system_off = value
+    self._infiltration_rate_area_for_ventilation_system_off = value
 
   @property
   def infiltration_rate_area_for_ventilation_system_on(self):
@@ -157,7 +157,7 @@ class ThermalArchetype:
     Get infiltration rate for ventilation system on in l/s/m2
     :return: float
     """
-    return self._infiltration_rate_for_ventilation_system_on
+    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):
@@ -165,4 +165,4 @@ class ThermalArchetype:
     Set infiltration rate for ventilation system on in l/s/m2
     :param value: float
     """
-    self._infiltration_rate_for_ventilation_system_on = value
+    self._infiltration_rate_area_for_ventilation_system_on = value

hub/city_model_structure/energy_systems/generation_system.py

@@ -11,6 +11,7 @@ from abc import ABC
 from typing import Union, List
 
 from hub.city_model_structure.energy_systems.distribution_system import DistributionSystem
+from hub.city_model_structure.energy_systems.energy_storage_system import EnergyStorageSystem
 from hub.city_model_structure.energy_systems.thermal_storage_system import ThermalStorageSystem
 from hub.city_model_structure.energy_systems.electrical_storage_system import ElectricalStorageSystem
 

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/exports/building_energy/idf.py

@@ -331,7 +331,6 @@ class Idf:
       for material in self._idf.idfobjects[self._MATERIAL]:
         if material.Name == "DefaultMaterial":
           return
-
       self._idf.set_default_constructions()
       return
     for layer in thermal_boundary.layers:
@@ -467,7 +466,7 @@ class Idf:
 
   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*1
+    _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,

hub/helpers/constants.py

@@ -24,6 +24,7 @@ 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
@@ -311,7 +312,8 @@ LATENT = 'Latent'
 LITHIUMION = 'Lithium Ion'
 NICD = 'NiCd'
 LEADACID = 'Lead Acid'
-
+THERMAL = 'thermal'
+ELECTRICAL = 'electrical'
 # Geometry
 EPSILON = 0.0000001
 

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/cerc_physics_parameters.py

@@ -69,6 +69,8 @@ class CercPhysicsParameters:
     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/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

@@ -81,6 +81,7 @@ class NrcanPhysicsParameters:
     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

@@ -136,13 +136,13 @@ 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_systems = []
         for emission_system in archetype_distribution_system.emission_systems:
-          _emission_system = EmissionSystem()
+          _generic_emission_system = EmissionSystem()
-          _emission_system.parasitic_energy_consumption = emission_system.parasitic_energy_consumption
+          _generic_emission_system.parasitic_energy_consumption = emission_system.parasitic_energy_consumption
-          _emission_systems.append(_emission_system)
+          _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:
+            _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'
-          _generation_system.energy_storage_systems = [_generic_storage_system]
+              _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
@@ -185,13 +192,13 @@ 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_systems = []
           for emission_system in archetype_distribution_system.emission_systems:
-            _emission_system = EmissionSystem()
+            _generic_emission_system = EmissionSystem()
-            _emission_system.parasitic_energy_consumption = emission_system.parasitic_energy_consumption
+            _generic_emission_system.parasitic_energy_consumption = emission_system.parasitic_energy_consumption
-            _emission_systems.append(_emission_system)
+            _emission_systems.append(_generic_emission_system)
           _distribution_system.emission_systems = _emission_systems
         _distribution_systems.append(_distribution_system)
       return _distribution_systems

input_files/omhm_selected_buildings.geojson

@@ -0,0 +1,16 @@
+{
+"type": "FeatureCollection",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
+"features": [
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+{ "type": "Feature", "properties": { "OBJECTID": 162727, "id": "01001804", "CIVIQUE_DE": 4535, "CIVIQUE_FI": 4535, "NOM_RUE": "avenue Henri-Julien  (MTL)", "MUNICIPALI": 50, "CODE_UTILI": 1000, "LIBELLE_UT": "Logement", "CATEGORIE_": "Régulier", "Hieght_LiD": 11, "AREA_NEW": 161, "MBG_Width": 12, "MBG_Length": 14, "MBG_Orientation": 118, "Shape_Length": 52, "Shape_Area": 169, "BuildingCategory": "semi-attached", "BuildingVolume": 1771, "AspectRatio": 1.118, "SurfacetoVolumeRatio": 0.091, "FloorNu_RawTax": 2, "FloorNu_RawTax.1": 2, "Floor_frmHieght": 3, "TotalFloorArea": 483, "ANNEE_CONS": 1980 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -73.584504328100977, 45.523318275073017 ], [ -73.584470063117109, 45.523357528940657 ], [ -73.584430877357136, 45.523409646947947 ], [ -73.584428076873849, 45.52341334646033 ], [ -73.584574755505997, 45.523468170484847 ], [ -73.584656456001568, 45.523374796559551 ], [ -73.584574577402094, 45.523346746210301 ], [ -73.584576876816001, 45.523343546476525 ], [ -73.584505377070585, 45.523318645930914 ], [ -73.584504328100977, 45.523318275073017 ] ] ] } },
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