Empty-Commit

costs/README.md

@@ -1,3 +1,4 @@
-# costs_workflow
+# Cerc costs 
 
-This workflow is a test to check that the proccess of calculating costs is correct before creating the API.
+Uses the cerc-hub as a base for cost calculation, it's intended to be used after executing the complete monthly energy
+balance workflow called building by building

costs/__init__.py

@@ -1,53 +1,9 @@
 """
 Cost workflow initialization
 """
-import glob
-import os
-from pathlib import Path
+from .capital_costs import CapitalCosts
+from .end_of_life_costs import EndOfLifeCosts
+from .total_maintenance_costs import TotalMaintenanceCosts
+from .total_operational_costs import TotalOperationalCosts
+from .total_operational_incomes import TotalOperationalIncomes
 
-# configurable parameters
-file_path = Path('./data/selected_building_2864.geojson').resolve()
-CONSTRUCTION_FORMAT = 'nrcan'
-USAGE_FORMAT = 'comnet'
-ENERGY_SYSTEM_FORMAT = 'montreal_custom'
-ATTIC_HEATED_CASE = 0
-BASEMENT_HEATED_CASE = 1
-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
-GAS_PRICE_INDEX = 0.05
-DISCOUNT_RATE = 0.03
-RETROFITTING_YEAR_CONSTRUCTION = 2020
-
-CLIMATE_REFERENCE_CITY = 'Montreal'
-WEATHER_FILE = 'CAN_PQ_Montreal.Intl.AP.716270_CWEC.epw'
-WEATHER_FORMAT = 'epw'
-CURRENT_STATUS = 0
-SKIN_RETROFIT = 1
-SYSTEM_RETROFIT_AND_PV = 2
-SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV = 3
-RETROFITTING_SCENARIOS = [
-  CURRENT_STATUS,
-  SKIN_RETROFIT,
-  SYSTEM_RETROFIT_AND_PV,
-  SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV
-]
-
-EMISSION_FACTOR_ELECTRICITY_QUEBEC = 0.0015 #https://www.cer-rec.gc.ca/en/data-analysis/energy-markets/provincial-territorial-energy-profiles/provincial-territorial-energy-profiles-quebec.html#:~:text=GHG%20Emissions,-Quebec's%20GHG%20emissions&text=The%20largest%20emitting%20sectors%20in,2.3%20MT%20CO2e.
-EMISSION_FACTOR_GAS_QUEBEC = 0.183 #https://www.canada.ca/en/environment-climate-change/services/climate-change/pricing-pollution-how-it-will-work/output-based-pricing-system/federal-greenhouse-gas-offset-system/emission-factors-reference-values.html
-EMISSION_FACTOR_BIOMASS_QUEBEC = 0.035 #Data from Spain. https://www.miteco.gob.es/es/cambio-climatico/temas/mitigacion-politicas-y-medidas/factoresemision_tcm30-479095.pdf
-EMISSION_FACTOR_FUEL_OIL_QUEBEC = 0.274
-EMISSION_FACTOR_DIESEL_QUEBEC = 0.240
-
-
-tmp_folder = Path('./tmp').resolve()
-out_path = Path('./outputs').resolve()
-files = glob.glob(f'{out_path}/*')
-print('path', file_path)
-for file in files:
-  if file != '.gitignore':
-    os.remove(file)

costs/__main__.py

@@ -4,251 +4,3 @@ SPDX - License - Identifier: LGPL - 3.0 - or -later
 Copyright © 2022 Project Author Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
 Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
 """
-
-from pathlib import Path
-
-import numpy_financial as npf
-import pandas as pd
-from hub.catalog_factories.costs_catalog_factory import CostCatalogFactory
-from hub.helpers.dictionaries import Dictionaries
-from hub.imports.construction_factory import ConstructionFactory
-from hub.imports.energy_systems_factory import EnergySystemsFactory
-from hub.imports.geometry_factory import GeometryFactory
-from hub.imports.usage_factory import UsageFactory
-from hub.imports.weather_factory import WeatherFactory
-from monthly_energy_balance_engine import MonthlyEnergyBalanceEngine
-from sra_engine import SraEngine
-from printing_results import *
-from hub.helpers import constants as cte
-from life_cycle_costs import LifeCycleCosts
-
-from costs import CONSTRUCTION_FORMAT
-from costs import ENERGY_SYSTEM_FORMAT, RETROFITTING_SCENARIOS, NUMBER_OF_YEARS
-from costs import CONSUMER_PRICE_INDEX, ELECTRICITY_PEAK_INDEX, ELECTRICITY_PRICE_INDEX, GAS_PRICE_INDEX, DISCOUNT_RATE
-from costs import SKIN_RETROFIT, SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV
-from costs import RETROFITTING_YEAR_CONSTRUCTION
-
-# import paths
-from results import Results
-
-
-def _npv_from_list(npv_discount_rate, list_cashflow):
-  lcc_value = npf.npv(npv_discount_rate, list_cashflow)
-  return lcc_value
-
-
-def _search_archetype(costs_catalog, building_function):
-  costs_archetypes = costs_catalog.entries('archetypes').archetypes
-  for building_archetype in costs_archetypes:
-    if str(building_function) == str(building_archetype.function):
-      return building_archetype
-  raise KeyError('archetype not found')
-
-
-life_cycle_results = pd.DataFrame()
-file_path = (Path(__file__).parent.parent / 'input_files' / 'summerschool_one_building.geojson')
-climate_reference_city = 'Montreal'
-weather_format = 'epw'
-construction_format = 'nrcan'
-usage_format = 'nrcan'
-energy_systems_format = 'montreal_custom'
-attic_heated_case = 0
-basement_heated_case = 1
-
-out_path = (Path(__file__).parent.parent / 'out_files')
-tmp_folder = (Path(__file__).parent / 'tmp')
-
-print('[simulation start]')
-city = GeometryFactory('geojson',
-                       path=file_path,
-                       height_field='citygml_me',
-                       year_of_construction_field='ANNEE_CONS',
-                       function_field='CODE_UTILI',
-                       function_to_hub=Dictionaries().montreal_function_to_hub_function).city
-city.climate_reference_city = climate_reference_city
-city.climate_file = (tmp_folder / f'{climate_reference_city}.cli').resolve()
-print(f'city created from {file_path}')
-WeatherFactory(weather_format, city).enrich()
-print('enrich weather... done')
-ConstructionFactory(construction_format, city).enrich()
-print('enrich constructions... done')
-UsageFactory(usage_format, city).enrich()
-print('enrich usage... done')
-for building in city.buildings:
-  building.energy_systems_archetype_name = 'system 1 gas pv'
-EnergySystemsFactory(energy_systems_format, city).enrich()
-print('enrich systems... done')
-
-print('exporting:')
-sra_file = (tmp_folder / f'{city.name}_sra.xml').resolve()
-SraEngine(city, sra_file, tmp_folder)
-print('    sra processed...')
-catalog = CostCatalogFactory('montreal_custom').catalog
-
-for retrofitting_scenario in RETROFITTING_SCENARIOS:
-
-  if retrofitting_scenario in (SKIN_RETROFIT, SYSTEM_RETROFIT_AND_PV):
-    for building in city.buildings:
-      building.year_of_construction = RETROFITTING_YEAR_CONSTRUCTION
-    ConstructionFactory(CONSTRUCTION_FORMAT, city).enrich()
-    print('enrich retrofitted constructions... done')
-
-  if retrofitting_scenario in (SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV):
-    for building in city.buildings:
-      building.energy_systems_archetype_name = 'system 6 electricity pv'
-    EnergySystemsFactory(ENERGY_SYSTEM_FORMAT, city).enrich()
-    print('enrich systems... done')
-
-  MonthlyEnergyBalanceEngine(city, tmp_folder)
-  print('    insel processed...')
-
-  for building in city.buildings:
-    for energy_system in building.energy_systems:
-      if cte.HEATING in energy_system.demand_types:
-        energy_system.generation_system.heat_power = building.heating_peak_load[cte.YEAR][0]
-      if cte.COOLING in energy_system.demand_types:
-        energy_system.generation_system.cooling_power = building.cooling_peak_load[cte.YEAR][0]
-    print(f' heating consumption {building.heating_consumption[cte.YEAR][0]}')
-  print('importing results:')
-  results = Results(city, out_path)
-  results.print()
-  print('results printed...')
-
-  print('[simulation end]')
-
-  print(f'beginning costing scenario {retrofitting_scenario} systems... done')
-
-  for building in city.buildings:
-    total_floor_area = 0
-    function = Dictionaries().hub_function_to_montreal_custom_costs_function[building.function]
-    archetype = _search_archetype(catalog, function)
-    print('lcc for first building started')
-    if "gas" in building.energy_systems_archetype_name:
-      FUEL_TYPE = 1
-    else:
-      FUEL_TYPE = 0
-
-    lcc = LifeCycleCosts(building, archetype, NUMBER_OF_YEARS, CONSUMER_PRICE_INDEX, ELECTRICITY_PEAK_INDEX,
-                         ELECTRICITY_PRICE_INDEX, GAS_PRICE_INDEX, DISCOUNT_RATE, retrofitting_scenario, FUEL_TYPE)
-    global_capital_costs, global_capital_incomes = lcc.calculate_capital_costs()
-    global_end_of_life_costs = lcc.calculate_end_of_life_costs()
-    global_operational_costs = lcc.calculate_total_operational_costs
-    global_maintenance_costs = lcc.calculate_total_maintenance_costs()
-    global_operational_incomes = lcc.calculate_total_operational_incomes(retrofitting_scenario)
-    full_path_output = Path(out_path / f'output {retrofitting_scenario} {building.name}.xlsx').resolve()
-    with pd.ExcelWriter(full_path_output) as writer:
-      global_capital_costs.to_excel(writer, sheet_name='global_capital_costs')
-      global_end_of_life_costs.to_excel(writer, sheet_name='global_end_of_life_costs')
-      global_operational_costs.to_excel(writer, sheet_name='global_operational_costs')
-      global_maintenance_costs.to_excel(writer, sheet_name='global_maintenance_costs')
-      global_operational_incomes.to_excel(writer, sheet_name='global_operational_incomes')
-      global_capital_incomes.to_excel(writer, sheet_name='global_capital_incomes')
-
-    investmentcosts = pd.DataFrame([])
-    print('RETROFITTING SCENARIO', retrofitting_scenario)
-    if retrofitting_scenario == 0:
-      investmentcosts = [global_capital_costs['B2010_opaque_walls'][0],
-                         global_capital_costs['B2020_transparent'][0],
-                         global_capital_costs['B3010_opaque_roof'][0],
-                         global_capital_costs['B10_superstructure'][0],
-                         global_capital_costs['D3020_heat_generating_systems'][0],
-                         global_capital_costs['D3080_other_hvac_ahu'][0],
-                         global_capital_costs['D5020_lighting_and_branch_wiring'][0],
-                         global_capital_costs['D301010_photovoltaic_system'][0]]
-      investmentcosts = pd.DataFrame(investmentcosts)
-
-    else:
-      investmentcosts[f'retrofitting_scenario_{retrofitting_scenario}'] = \
-        [global_capital_costs['B2010_opaque_walls'][0],
-         global_capital_costs['B2020_transparent'][0],
-         global_capital_costs['B3010_opaque_roof'][0],
-         global_capital_costs['B10_superstructure'][0],
-         global_capital_costs['D3020_heat_generating_systems'][0],
-         global_capital_costs['D3080_other_hvac_ahu'][0],
-         global_capital_costs['D5020_lighting_and_branch_wiring'][0],
-         global_capital_costs['D301010_photovoltaic_system'][0]]
-
-    investmentcosts.index = ['Opaque walls', 'Transparent walls', 'Opaque roof', 'Superstructure',
-                             'Heat generation systems', 'Other HVAC AHU', 'Lighting and branch wiring', 'PV systems']
-
-    df_capital_costs_skin = (
-        global_capital_costs['B2010_opaque_walls'] + global_capital_costs['B2020_transparent'] +
-        global_capital_costs['B3010_opaque_roof'] + global_capital_costs['B10_superstructure']
-    )
-    df_capital_costs_systems = (
-        global_capital_costs['D3020_heat_generating_systems'] +
-        global_capital_costs['D3030_cooling_generation_systems'] +
-        global_capital_costs['D3080_other_hvac_ahu'] +
-        global_capital_costs['D5020_lighting_and_branch_wiring'] +
-        global_capital_costs['D301010_photovoltaic_system']
-    )
-    df_end_of_life_costs = global_end_of_life_costs['End_of_life_costs']
-    df_operational_costs = (
-      global_operational_costs['Fixed_costs_electricity_peak'] +
-      global_operational_costs['Fixed_costs_electricity_monthly'] +
-      global_operational_costs['Fixed_costs_electricity_peak'] +
-      global_operational_costs['Fixed_costs_electricity_monthly'] +
-      global_operational_costs['Variable_costs_electricity'] +
-      global_operational_costs['Fixed_costs_gas'] +
-      global_operational_costs['Variable_costs_gas']
-    )
-    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 = _npv_from_list(DISCOUNT_RATE, df_capital_costs_skin.values.tolist())
-    life_cycle_costs_capital_systems = _npv_from_list(DISCOUNT_RATE, df_capital_costs_systems.values.tolist())
-    life_cycle_costs_end_of_life_costs = _npv_from_list(DISCOUNT_RATE, df_end_of_life_costs.values.tolist())
-    life_cycle_operational_costs = _npv_from_list(DISCOUNT_RATE, df_operational_costs.values.tolist())
-    life_cycle_maintenance_costs = _npv_from_list(DISCOUNT_RATE, df_maintenance_costs.values.tolist())
-    life_cycle_operational_incomes = _npv_from_list(DISCOUNT_RATE, df_operational_incomes.values.tolist())
-    life_cycle_capital_incomes = _npv_from_list(DISCOUNT_RATE, df_capital_incomes.values.tolist())
-
-    life_cycle_costs = (
-        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
-    )
-    total_floor_area += lcc.calculate_total_floor_area()
-    life_cycle_results[f'Scenario {retrofitting_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]
-
-    life_cycle_results.index = ['total_capital_costs_skin',
-                                'total_capital_costs_systems',
-                                'end_of_life_costs',
-                                'total_operational_costs',
-                                'total_maintenance_costs',
-                                'operational_incomes',
-                                'capital_incomes']
-
-    print(f'Scenario {retrofitting_scenario} {life_cycle_costs}')
-
-#    printing_results(investmentcosts, life_cycle_results, total_floor_area)
-
-
-
-
-
-
-
-
-
-

costs/__main__emissions.py

@@ -1,68 +0,0 @@
-"""
-Costs Workflow
-SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2022 Project Author Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
-Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
-"""
-
-from pathlib import Path
-
-import pandas as pd
-from hub.helpers.dictionaries import Dictionaries
-from hub.catalog_factories.costs_catalog_factory import CostCatalogFactory
-
-from costs import EMISSION_FACTOR_ELECTRICITY_QUEBEC, EMISSION_FACTOR_GAS_QUEBEC, EMISSION_FACTOR_BIOMASS_QUEBEC, \
-  EMISSION_FACTOR_FUEL_OIL_QUEBEC, EMISSION_FACTOR_DIESEL_QUEBEC, NUMBER_OF_YEARS
-
-def _search_archetype(costs_catalog, building_function):
-  costs_archetypes = costs_catalog.entries('archetypes').archetypes
-  for building_archetype in costs_archetypes:
-    if str(building_function) == str(building_archetype.function):
-      return building_archetype
-  raise KeyError('archetype not found')
-
-catalog = CostCatalogFactory('montreal_custom').catalog
-
-for building in city.buildings:
-  building_heating_consumption = 1000
-  building_domestic_water_consumption = 1000
-  building_cooling_consumption = 1000
-  distribution_systems_electrical_consumption = 1000
-  lighting_electrical_demand = 1000
-  appliances_electrical_demand = 1000
-  rng = range(NUMBER_OF_YEARS)
-
-  function = Dictionaries().hub_function_to_montreal_custom_costs_function[building.function]
-  archetype = _search_archetype(catalog, function)
-
-  print('co2 for first building started')
-  if "gas" in building.energy_systems_archetype_name:
-    gas_consumption = building_heating_consumption + building_domestic_water_consumption
-    electricity_consumption = building_cooling_consumption + distribution_systems_electrical_consumption + \
-                              lighting_electrical_demand + appliances_electrical_demand
-    biomass_consumption = 0
-    fuel_oil_consumption = 0
-    diesel_consumption = 0
-  else:
-    gas_consumption = 0
-    electricity_consumption = building_heating_consumption + building_domestic_water_consumption + \
-                              building_cooling_consumption + distribution_systems_electrical_consumption + \
-                              lighting_electrical_demand + appliances_electrical_demand
-    biomass_consumption = 0
-    fuel_oil_consumption = 0
-    diesel_consumption = 0
-
-  CO2_emissions = pd.DataFrame(index=rng, columns=['CO2 emissions gas', 'CO2 emissions electricity',
-                                                   'CO2 Emissions biomass', 'CO2 emissions fueloil',
-                                                   'CO2 emissions diesel'], dtype='float')
-
-  for year in range(1, NUMBER_OF_YEARS+1):
-
-    CO2_emissions.at[year,'CO2 emissions gas'] = gas_consumption * EMISSION_FACTOR_GAS_QUEBEC
-    CO2_emissions.at[year, 'CO2 emissions electricity'] = electricity_consumption * EMISSION_FACTOR_ELECTRICITY_QUEBEC
-    CO2_emissions.at[year, 'CO2 emissions biomass'] = biomass_consumption * EMISSION_FACTOR_BIOMASS_QUEBEC
-    CO2_emissions.at[year, 'CO2 emissions fueloil'] = fuel_oil_consumption * EMISSION_FACTOR_FUEL_OIL_QUEBEC
-    CO2_emissions.at[year, 'CO2 emissions diesel'] = diesel_consumption * EMISSION_FACTOR_DIESEL_QUEBEC
-
-  CO2_emissions_total = CO2_emissions.sum()
-

costs/capital_costs.py

@@ -0,0 +1,236 @@
+"""
+Capital costs module
+"""
+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 costs.configuration import Configuration
+from costs.constants import SKIN_RETROFIT, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT_AND_PV
+from costs.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',
+        'B10_superstructure',
+        'D301010_photovoltaic_system',
+        'D3020_heat_generating_systems',
+        'D3030_cooling_generation_systems',
+        'D3040_distribution_systems',
+        'D3080_other_hvac_ahu',
+        'D5020_lighting_and_branch_wiring'
+      ],
+      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]['B10_superstructure'] = 0
+    self._yearly_capital_costs.loc[0, 'D3020_heat_generating_systems'] = 0
+    self._yearly_capital_costs.loc[0, 'D3030_cooling_generation_systems'] = 0
+    self._yearly_capital_costs.loc[0, 'D3040_distribution_systems'] = 0
+    self._yearly_capital_costs.loc[0, 'D3080_other_hvac_ahu'] = 0
+    self._yearly_capital_costs.loc[0, 'D5020_lighting_and_branch_wiring'] = 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
+
+  def calculate(self) -> tuple[pd.DataFrame, pd.DataFrame]:
+    """
+    Calculate capital cost
+    :return: pd.DataFrame, pd.DataFrame
+    """
+    surface_opaque = 0
+    surface_transparent = 0
+    surface_roof = 0
+    surface_ground = 0
+    capital_cost_pv = 0
+    capital_cost_opaque = 0
+    capital_cost_ground = 0
+    capital_cost_transparent = 0
+    capital_cost_roof = 0
+    capital_cost_heating_equipment = 0
+    capital_cost_cooling_equipment = 0
+    capital_cost_distribution_equipment = 0
+    capital_cost_other_hvac_ahu = 0
+    capital_cost_lighting = 0
+
+    for internal_zone in self._building.internal_zones:
+      for thermal_zone in internal_zone.thermal_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
+
+    peak_heating = self._building.heating_peak_load[cte.YEAR][0] / 1000
+    peak_cooling = self._building.cooling_peak_load[cte.YEAR][0] / 1000
+
+    surface_pv = 0
+    for roof in self._building.roofs:
+      surface_pv += roof.solid_polygon.area * roof.solar_collectors_area_reduction_factor
+
+    self._yearly_capital_costs.fillna(0, inplace=True)
+    own_capital = 1 - self._configuration.percentage_credit
+    if self._configuration.retrofit_scenario in (SKIN_RETROFIT, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV):
+      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('B10_superstructure').refurbishment[0]
+      self._yearly_capital_costs.loc[0, 'B2010_opaque_walls'] = capital_cost_opaque * own_capital
+      self._yearly_capital_costs.loc[0]['B2020_transparent'] = capital_cost_transparent * own_capital
+      self._yearly_capital_costs.loc[0, 'B3010_opaque_roof'] = capital_cost_roof * own_capital
+      self._yearly_capital_costs.loc[0]['B10_superstructure'] = capital_cost_ground * own_capital
+
+    if self._configuration.retrofit_scenario in (SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV):
+      chapter = self._capital_costs_chapter.chapter('D_services')
+      capital_cost_pv = surface_pv * chapter.item('D301010_photovoltaic_system').initial_investment[0]
+      capital_cost_heating_equipment = peak_heating * chapter.item('D3020_heat_generating_systems').initial_investment[0]
+      capital_cost_cooling_equipment = peak_cooling * chapter.item('D3030_cooling_generation_systems').initial_investment[0]
+      capital_cost_distribution_equipment = peak_cooling * chapter.item('D3040_distribution_systems').initial_investment[0]
+      capital_cost_other_hvac_ahu = peak_cooling * chapter.item('D3080_other_hvac_ahu').initial_investment[0]
+      capital_cost_lighting = self._total_floor_area * chapter.item('D5020_lighting_and_branch_wiring').initial_investment[0]
+      self._yearly_capital_costs.loc[0]['D301010_photovoltaic_system'] = capital_cost_pv
+      self._yearly_capital_costs.loc[0, 'D3020_heat_generating_systems'] = capital_cost_heating_equipment * own_capital
+      self._yearly_capital_costs.loc[0, 'D3030_cooling_generation_systems'] = capital_cost_cooling_equipment * own_capital
+      self._yearly_capital_costs.loc[0, 'D3040_distribution_systems'] = capital_cost_distribution_equipment * own_capital
+      self._yearly_capital_costs.loc[0, 'D3080_other_hvac_ahu'] = capital_cost_other_hvac_ahu * own_capital
+      self._yearly_capital_costs.loc[0, 'D5020_lighting_and_branch_wiring'] = capital_cost_lighting * own_capital
+
+    for year in range(1, self._configuration.number_of_years):
+      chapter = self._capital_costs_chapter.chapter('D_services')
+      costs_increase = math.pow(1 + self._configuration.consumer_price_index, year)
+      self._yearly_capital_costs.loc[year, 'B2010_opaque_walls'] = (
+        -npf.pmt(
+          self._configuration.interest_rate,
+          self._configuration.credit_years,
+          capital_cost_opaque * self._configuration.percentage_credit
+        )
+      )
+      self._yearly_capital_costs.loc[year, 'B2020_transparent'] = (
+        -npf.pmt(
+          self._configuration.interest_rate,
+          self._configuration.credit_years,
+          capital_cost_transparent * self._configuration.percentage_credit
+        )
+      )
+      self._yearly_capital_costs.loc[year, 'B3010_opaque_roof'] = (
+        -npf.pmt(
+          self._configuration.interest_rate,
+          self._configuration.credit_years,
+          capital_cost_roof * self._configuration.percentage_credit
+        )
+      )
+      self._yearly_capital_costs.loc[year, 'B10_superstructure'] = (
+        -npf.pmt(
+          self._configuration.interest_rate,
+          self._configuration.credit_years,
+          capital_cost_ground * self._configuration.percentage_credit
+        )
+      )
+      self._yearly_capital_costs.loc[year, 'D3020_heat_generating_systems'] = (
+        -npf.pmt(
+          self._configuration.interest_rate,
+          self._configuration.credit_years,
+          capital_cost_heating_equipment * self._configuration.percentage_credit
+        )
+      )
+      self._yearly_capital_costs.loc[year, 'D3030_cooling_generation_systems'] = (
+        -npf.pmt(
+          self._configuration.interest_rate,
+          self._configuration.credit_years,
+          capital_cost_cooling_equipment * self._configuration.percentage_credit
+        )
+      )
+      self._yearly_capital_costs.loc[year, 'D3040_distribution_systems'] = (
+        -npf.pmt(
+          self._configuration.interest_rate,
+          self._configuration.credit_years,
+          capital_cost_distribution_equipment * self._configuration.percentage_credit
+        )
+      )
+      self._yearly_capital_costs.loc[year, 'D3080_other_hvac_ahu'] = (
+        -npf.pmt(
+          self._configuration.interest_rate,
+          self._configuration.credit_years,
+          capital_cost_other_hvac_ahu * self._configuration.percentage_credit
+        )
+      )
+      self._yearly_capital_costs.loc[year, 'D5020_lighting_and_branch_wiring'] = (
+        -npf.pmt(
+          self._configuration.interest_rate,
+          self._configuration.credit_years,
+          capital_cost_lighting * self._configuration.percentage_credit
+        )
+      )
+
+      if (year % chapter.item('D3020_heat_generating_systems').lifetime) == 0:
+        reposition_cost_heating_equipment = (
+            peak_heating * chapter.item('D3020_heat_generating_systems').reposition[0] * costs_increase
+        )
+        self._yearly_capital_costs.loc[year, 'D3020_heat_generating_systems'] += reposition_cost_heating_equipment
+
+      if (year % chapter.item('D3030_cooling_generation_systems').lifetime) == 0:
+        reposition_cost_cooling_equipment = (
+            peak_cooling * chapter.item('D3030_cooling_generation_systems').reposition[0] * costs_increase
+        )
+        self._yearly_capital_costs.loc[year, 'D3030_cooling_generation_systems'] += reposition_cost_cooling_equipment
+
+      if (year % chapter.item('D3080_other_hvac_ahu').lifetime) == 0:
+        reposition_cost_hvac_ahu = (
+            peak_cooling * chapter.item('D3080_other_hvac_ahu').reposition[0] * costs_increase
+        )
+        self._yearly_capital_costs.loc[year, 'D3080_other_hvac_ahu'] = reposition_cost_hvac_ahu
+
+      if (year % chapter.item('D5020_lighting_and_branch_wiring').lifetime) == 0:
+        reposition_cost_lighting = (
+            self._total_floor_area * chapter.item('D5020_lighting_and_branch_wiring').reposition[0] * costs_increase
+        )
+        self._yearly_capital_costs.loc[year, 'D5020_lighting_and_branch_wiring'] += reposition_cost_lighting
+
+      if self._configuration.retrofit_scenario in (SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV):
+        if (year % chapter.item('D301010_photovoltaic_system').lifetime) == 0:
+          self._yearly_capital_costs.loc[year]['D301010_photovoltaic_system'] += (
+              surface_pv * chapter.item('D301010_photovoltaic_system').reposition[0] * costs_increase
+          )
+    capital_cost_skin = capital_cost_opaque + capital_cost_ground + capital_cost_transparent + capital_cost_roof
+    capital_cost_hvac = (
+        capital_cost_heating_equipment +
+        capital_cost_cooling_equipment +
+        capital_cost_distribution_equipment +
+        capital_cost_other_hvac_ahu + capital_cost_lighting
+    )
+
+    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)
+    return self._yearly_capital_costs, self._yearly_capital_incomes

costs/configuration.py

@@ -0,0 +1,225 @@
+"""
+Configuration module
+"""
+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
+               ):
+    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
+
+  @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

costs/constants.py

@@ -0,0 +1,11 @@
+# constants
+CURRENT_STATUS = 0
+SKIN_RETROFIT = 1
+SYSTEM_RETROFIT_AND_PV = 2
+SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV = 3
+RETROFITTING_SCENARIOS = [
+  CURRENT_STATUS,
+  SKIN_RETROFIT,
+  SYSTEM_RETROFIT_AND_PV,
+  SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV
+]

costs/cost.py

@@ -0,0 +1,143 @@
+"""
+Cost module
+"""
+import hub.helpers.dictionaries
+import pandas as pd
+import numpy_financial as npf
+from hub.city_model_structure.building import Building
+
+from costs.configuration import Configuration
+from costs import CapitalCosts, EndOfLifeCosts, TotalMaintenanceCosts, TotalOperationalCosts, TotalOperationalIncomes
+from costs.constants import CURRENT_STATUS
+
+
+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,
+               gas_price_index=0.05,
+               discount_rate=0.03,
+               retrofitting_year_construction=2020,
+               factories_handler='montreal_custom',
+               retrofit_scenario=CURRENT_STATUS,
+               dictionary=hub.helpers.dictionaries.Dictionaries().hub_function_to_montreal_custom_costs_function):
+    self._building = building
+    fuel_type = 0
+    if "gas" in building.energy_systems_archetype_name:
+      fuel_type = 1
+    self._configuration = Configuration(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)
+
+  @property
+  def building(self) -> Building:
+    """
+    Get current building.
+    """
+    return self._building
+
+  @building.setter
+  def building(self, value: Building):
+    """
+    Set current building.
+    """
+    self._building = value
+
+  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['B10_superstructure']
+    )
+    df_capital_costs_systems = (
+        global_capital_costs['D3020_heat_generating_systems'] +
+        global_capital_costs['D3030_cooling_generation_systems'] +
+        global_capital_costs['D3080_other_hvac_ahu'] +
+        global_capital_costs['D5020_lighting_and_branch_wiring'] +
+        global_capital_costs['D301010_photovoltaic_system']
+    )
+    df_end_of_life_costs = global_end_of_life_costs['End_of_life_costs']
+    df_operational_costs = (
+        global_operational_costs['Fixed_costs_electricity_peak'] +
+        global_operational_costs['Fixed_costs_electricity_monthly'] +
+        global_operational_costs['Fixed_costs_electricity_peak'] +
+        global_operational_costs['Fixed_costs_electricity_monthly'] +
+        global_operational_costs['Variable_costs_electricity'] +
+        global_operational_costs['Fixed_costs_gas'] +
+        global_operational_costs['Variable_costs_gas']
+    )
+    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.values.tolist())
+    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
+    ]
+
+    results.index = ['total_capital_costs_skin',
+                     'total_capital_costs_systems',
+                     'end_of_life_costs',
+                     'total_operational_costs',
+                     'total_maintenance_costs',
+                     'operational_incomes',
+                     'capital_incomes']
+
+    return results

costs/cost_base.py

@@ -0,0 +1,38 @@
+"""
+Cost base module
+"""
+
+from hub.city_model_structure.building import Building
+from hub.helpers.dictionaries import Dictionaries
+
+from costs.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 internal_zone in building.internal_zones:
+      for thermal_zone in internal_zone.thermal_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()

costs/data/.gitignore

@@ -1,4 +0,0 @@
-# Ignore everything in this directory
-*
-# Except this file
-!.gitignore

costs/end_of_life_costs.py

@@ -0,0 +1,34 @@
+"""
+End of life costs module
+"""
+import math
+import pandas as pd
+from hub.city_model_structure.building import Building
+
+from costs.configuration import Configuration
+from costs.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

costs/life_cycle_costs.py

@@ -1,375 +0,0 @@
-"""
-LifeCycleCosts module calculates the life cycle costs of one building
-SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2022 Project Author 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
-import hub.helpers.constants as cte
-from costs import SKIN_RETROFIT, SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, PERCENTAGE_CREDIT,INTEREST_RATE,CREDIT_YEARS
-
-
-class LifeCycleCosts:
-  """
-  Life cycle cost class
-  """
-
-  def __init__(self, building, archetype, number_of_years, consumer_price_index, electricity_peak_index,
-               electricity_price_index, gas_price_index, discount_rate,
-               retrofitting_scenario, fuel_type):
-    self._building = building
-    self._number_of_years = number_of_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._archetype = archetype
-    self._end_of_life_cost = 0
-    self._capital_costs_at_year_0 = 0
-    self._items = 0
-    self._fuels = 0
-    self._concepts = 0
-    self._retrofitting_scenario = retrofitting_scenario
-    self._total_floor_area = 0
-    self._fuel_type = fuel_type
-    for internal_zone in building.internal_zones:
-      for thermal_zone in internal_zone.thermal_zones:
-        self._total_floor_area += thermal_zone.total_floor_area
-
-    # todo: revise if it works
-    rng = range(number_of_years)
-    self._yearly_capital_costs = pd.DataFrame(index=rng, columns=['B2010_opaque_walls', 'B2020_transparent',
-                                                                  'B3010_opaque_roof', 'B10_superstructure',
-                                                                  'D301010_photovoltaic_system',
-                                                                  'D3020_heat_generating_systems',
-                                                                  'D3030_cooling_generation_systems',
-                                                                  'D3040_distribution_systems',
-                                                                  'D3080_other_hvac_ahu',
-                                                                  'D5020_lighting_and_branch_wiring'],
-                                              dtype='float')
-    self._yearly_end_of_life_costs = pd.DataFrame(index=rng, columns=['End_of_life_costs'], dtype='float')
-    self._yearly_operational_costs = pd.DataFrame(index=rng, columns=['Fixed_costs_electricity_peak',
-                                                                      'Fixed_costs_electricity_monthly',
-                                                                      'Variable_costs_electricity', 'Fixed_costs_gas',
-                                                                      'Variable_costs_gas'],
-                                                  dtype='float')
-    self._yearly_maintenance_costs = pd.DataFrame(index=rng, columns=['Heating_maintenance', 'Cooling_maintenance',
-                                                                      'PV_maintenance'], dtype='float')
-    self._yearly_operational_incomes = pd.DataFrame(index=rng, columns=['Incomes electricity'], dtype='float')
-
-    self._yearly_capital_incomes = pd.DataFrame(index=rng, columns=['Subsidies construction',
-                                                                    'Subsidies HVAC', 'Subsidies PV'], dtype='float')
-
-  def calculate_capital_costs(self):
-    """
-    Calculate capital cost
-    :return: pd.DataFrame
-    """
-    building = self._building
-    archetype = self._archetype
-
-    surface_opaque = 0
-    surface_transparent = 0
-    surface_roof = 0
-    surface_ground = 0
-    capital_cost_pv = 0
-    capital_cost_opaque = 0
-    capital_cost_ground = 0
-    capital_cost_transparent = 0
-    capital_cost_roof = 0
-    capital_cost_heating_equipment = 0
-    capital_cost_cooling_equipment = 0
-    capital_cost_distribution_equipment = 0
-    capital_cost_other_hvac_ahu = 0
-    capital_cost_lighting = 0
-
-    total_floor_area = self._total_floor_area
-
-    for internal_zone in building.internal_zones:
-      for thermal_zone in internal_zone.thermal_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
-
-    chapters = archetype.capital_cost
-    peak_heating = building.heating_peak_load[cte.YEAR][0]/1000
-    peak_cooling = building.cooling_peak_load[cte.YEAR][0]/1000
-    # todo: change area pv when the variable exists
-    roof_area = 0
-    for roof in building.roofs:
-      roof_area += roof.solid_polygon.area
-    surface_pv = roof_area * 0.5
-
-    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]['B10_superstructure'] = 0
-
-    self._yearly_capital_costs.loc[0, 'D3020_heat_generating_systems'] = 0
-    self._yearly_capital_costs.loc[0, 'D3030_cooling_generation_systems'] = 0
-    self._yearly_capital_costs.loc[0, 'D3040_distribution_systems'] = 0
-    self._yearly_capital_costs.loc[0, 'D3080_other_hvac_ahu'] = 0
-    self._yearly_capital_costs.loc[0, 'D5020_lighting_and_branch_wiring'] = 0
-
-    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)
-    if self._retrofitting_scenario in (SKIN_RETROFIT, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV):
-      chapter = chapters.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('B10_superstructure').refurbishment[0]
-
-
-      self._yearly_capital_costs.loc[0, 'B2010_opaque_walls'] = capital_cost_opaque * (1-PERCENTAGE_CREDIT)
-      self._yearly_capital_costs.loc[0]['B2020_transparent'] = capital_cost_transparent * (1-PERCENTAGE_CREDIT)
-      self._yearly_capital_costs.loc[0, 'B3010_opaque_roof'] = capital_cost_roof * (1-PERCENTAGE_CREDIT)
-      self._yearly_capital_costs.loc[0]['B10_superstructure'] = capital_cost_ground * (1-PERCENTAGE_CREDIT)
-
-
-    if self._retrofitting_scenario in (SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV):
-      chapter = chapters.chapter('D_services')
-      capital_cost_pv = surface_pv * chapter.item('D301010_photovoltaic_system').initial_investment[0]
-      self._yearly_capital_costs.loc[0]['D301010_photovoltaic_system'] = capital_cost_pv
-      capital_cost_heating_equipment = (
-          peak_heating * chapter.item('D3020_heat_generating_systems').initial_investment[0]
-      )
-      capital_cost_cooling_equipment = (
-          peak_cooling * chapter.item('D3030_cooling_generation_systems').initial_investment[0]
-      )
-      capital_cost_distribution_equipment = (
-          peak_cooling * chapter.item('D3040_distribution_systems').initial_investment[0]
-      )
-      capital_cost_other_hvac_ahu = peak_cooling * chapter.item('D3080_other_hvac_ahu').initial_investment[0]
-      capital_cost_lighting = total_floor_area * chapter.item('D5020_lighting_and_branch_wiring').initial_investment[0]
-
-      self._yearly_capital_costs.loc[0, 'D3020_heat_generating_systems'] = capital_cost_heating_equipment * (1-PERCENTAGE_CREDIT)
-      self._yearly_capital_costs.loc[0, 'D3030_cooling_generation_systems'] = capital_cost_cooling_equipment * (1-PERCENTAGE_CREDIT)
-      self._yearly_capital_costs.loc[0, 'D3040_distribution_systems'] = capital_cost_distribution_equipment * (1-PERCENTAGE_CREDIT)
-      self._yearly_capital_costs.loc[0, 'D3080_other_hvac_ahu'] = capital_cost_other_hvac_ahu * (1-PERCENTAGE_CREDIT)
-      self._yearly_capital_costs.loc[0, 'D5020_lighting_and_branch_wiring'] = capital_cost_lighting * (1-PERCENTAGE_CREDIT)
-
-    for year in range(1, self._number_of_years):
-      chapter = chapters.chapter('D_services')
-      costs_increase = math.pow(1 + self._consumer_price_index, year)
-      self._yearly_capital_costs.loc[year, 'B2010_opaque_walls'] = -npf.pmt(INTEREST_RATE, CREDIT_YEARS,
-                                                                          capital_cost_opaque * (PERCENTAGE_CREDIT))
-      self._yearly_capital_costs.loc[year, 'B2020_transparent'] = -npf.pmt(INTEREST_RATE, CREDIT_YEARS,
-                                                                          capital_cost_transparent * (PERCENTAGE_CREDIT)
-                                                                          )
-      self._yearly_capital_costs.loc[year, 'B3010_opaque_roof'] = -npf.pmt(INTEREST_RATE, CREDIT_YEARS,capital_cost_roof
-                                                                          * (PERCENTAGE_CREDIT))
-      self._yearly_capital_costs.loc[year, 'B10_superstructure'] = -npf.pmt(INTEREST_RATE, CREDIT_YEARS,
-                                                                             capital_cost_ground * (PERCENTAGE_CREDIT))
-      self._yearly_capital_costs.loc[year, 'D3020_heat_generating_systems'] = -npf.pmt(INTEREST_RATE,CREDIT_YEARS,
-                                                                                      capital_cost_heating_equipment
-                                                                                      * (PERCENTAGE_CREDIT))
-      self._yearly_capital_costs.loc[year, 'D3030_cooling_generation_systems'] = -npf.pmt(INTEREST_RATE, CREDIT_YEARS,
-                                                                                      capital_cost_cooling_equipment
-                                                                                      * (PERCENTAGE_CREDIT))
-      self._yearly_capital_costs.loc[year, 'D3040_distribution_systems'] = -npf.pmt(INTEREST_RATE, CREDIT_YEARS,
-                                                                                      capital_cost_distribution_equipment
-                                                                                      * (PERCENTAGE_CREDIT))
-      self._yearly_capital_costs.loc[year, 'D3080_other_hvac_ahu'] = -npf.pmt(INTEREST_RATE, CREDIT_YEARS,
-                                                                                      capital_cost_other_hvac_ahu
-                                                                                      * (PERCENTAGE_CREDIT))
-      self._yearly_capital_costs.loc[year, 'D5020_lighting_and_branch_wiring'] = -npf.pmt(INTEREST_RATE, CREDIT_YEARS,
-                                                                                      capital_cost_lighting
-                                                                                      * (PERCENTAGE_CREDIT))
-      if (year % chapter.item('D3020_heat_generating_systems').lifetime) == 0:
-        reposition_cost_heating_equipment = peak_heating * chapter.item('D3020_heat_generating_systems').reposition[0] \
-                                            * costs_increase
-        self._yearly_capital_costs.loc[year, 'D3020_heat_generating_systems'] += reposition_cost_heating_equipment
-
-      if (year % chapter.item('D3030_cooling_generation_systems').lifetime) == 0:
-        reposition_cost_cooling_equipment = peak_cooling \
-                                            * chapter.item('D3030_cooling_generation_systems').reposition[0] \
-                                            * costs_increase
-        self._yearly_capital_costs.loc[year, 'D3030_cooling_generation_systems'] += reposition_cost_cooling_equipment
-
-      if (year % chapter.item('D3080_other_hvac_ahu').lifetime) == 0:
-        reposition_cost_hvac_ahu = peak_cooling * chapter.item('D3080_other_hvac_ahu').reposition[0] * costs_increase
-        self._yearly_capital_costs.loc[year, 'D3080_other_hvac_ahu'] = reposition_cost_hvac_ahu
-
-      if (year % chapter.item('D5020_lighting_and_branch_wiring').lifetime) == 0:
-        reposition_cost_lighting = total_floor_area * chapter.item('D5020_lighting_and_branch_wiring').reposition[0] \
-                                   * costs_increase
-        self._yearly_capital_costs.loc[year, 'D5020_lighting_and_branch_wiring'] += reposition_cost_lighting
-
-      if self._retrofitting_scenario in (SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV):
-        if (year % chapter.item('D301010_photovoltaic_system').lifetime) == 0:
-          self._yearly_capital_costs.loc[year]['D301010_photovoltaic_system'] += surface_pv \
-                                                                                * chapter.item(
-            'D301010_photovoltaic_system').reposition[0] * costs_increase
-    capital_cost_skin = capital_cost_opaque + capital_cost_ground + capital_cost_transparent + capital_cost_roof
-    capital_cost_hvac = (
-        capital_cost_heating_equipment +
-        capital_cost_cooling_equipment +
-        capital_cost_distribution_equipment +
-        capital_cost_other_hvac_ahu + capital_cost_lighting
-    )
-
-    self._yearly_capital_incomes.loc[0, 'Subsidies construction'] = (
-        capital_cost_skin * archetype.income.construction_subsidy/100
-    )
-    self._yearly_capital_incomes.loc[0, 'Subsidies HVAC'] = capital_cost_hvac * archetype.income.hvac_subsidy/100
-    self._yearly_capital_incomes.loc[0, 'Subsidies PV'] = capital_cost_pv * archetype.income.photovoltaic_subsidy/100
-    self._yearly_capital_incomes.fillna(0, inplace=True)
-    return self._yearly_capital_costs, self._yearly_capital_incomes
-
-  def calculate_end_of_life_costs(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._number_of_years + 1):
-      price_increase = math.pow(1 + self._consumer_price_index, year)
-      if year == self._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
-
-  def calculate_total_floor_area(self):
-    total_floor_area = self._total_floor_area
-    return total_floor_area
-  @property
-  def calculate_total_operational_costs(self):
-    """
-    Calculate total operational costs
-    :return: pd.DataFrame
-    """
-    building = self._building
-    archetype = self._archetype
-    total_floor_area = self._total_floor_area
-    factor_residential = total_floor_area / 80
-    # todo: split the heating between fuels
-    fixed_gas_cost_year_0 = 0
-    variable_gas_cost_year_0 = 0
-    electricity_heating = 0
-    domestic_hot_water_electricity = 0
-    if self._fuel_type == 1:
-      fixed_gas_cost_year_0 = archetype.operational_cost.fuels[1].fixed_monthly * 12 * factor_residential
-      variable_gas_cost_year_0 = (
-          (building.heating_consumption[cte.YEAR][0] + building.domestic_hot_water_consumption[cte.YEAR][0]) / 1000 *
-          archetype.operational_cost.fuels[1].variable[0]
-      )
-    if self._fuel_type == 0:
-      electricity_heating = building.heating_consumption[cte.YEAR][0] / 1000
-      domestic_hot_water_electricity = building.domestic_hot_water_consumption[cte.YEAR][0] / 1000
-
-    electricity_cooling = building.cooling_consumption[cte.YEAR][0] / 1000
-    electricity_lighting = building.lighting_electrical_demand[cte.YEAR]['insel meb'] / 1000
-    electricity_plug_loads = building.appliances_electrical_demand[cte.YEAR]['insel meb'] / 1000
-    electricity_distribution = 0
-    total_electricity_consumption = (
-        electricity_heating + electricity_cooling + electricity_lighting + domestic_hot_water_electricity +
-        electricity_plug_loads + electricity_distribution
-    )
-    print(f'electricity consumption {total_electricity_consumption}')
-
-    # todo: change when peak electricity demand is coded. Careful with factor residential
-    peak_electricity_demand = 0.1*total_floor_area # self._peak_electricity_demand
-    variable_electricity_cost_year_0 = total_electricity_consumption * archetype.operational_cost.fuels[0].variable[0]
-    peak_electricity_cost_year_0 = peak_electricity_demand * archetype.operational_cost.fuels[0].fixed_power * 12
-    monthly_electricity_cost_year_0 = archetype.operational_cost.fuels[0].fixed_monthly * 12 * factor_residential
-
-    for year in range(1, self._number_of_years + 1):
-      price_increase_electricity = math.pow(1 + self._electricity_price_index, year)
-      price_increase_peak_electricity = math.pow(1 + self._electricity_peak_index, year)
-      price_increase_gas = math.pow(1 + self._gas_price_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
-      )
-      self._yearly_operational_costs.at[year, 'Variable_costs_electricity'] = float(
-        variable_electricity_cost_year_0 * price_increase_electricity
-      )
-      self._yearly_operational_costs.at[year, 'Fixed_costs_gas'] = fixed_gas_cost_year_0 * price_increase_gas
-      self._yearly_operational_costs.at[year, 'Variable_costs_gas'] = (
-          variable_gas_cost_year_0 * price_increase_peak_electricity
-      )
-      self._yearly_operational_costs.at[year, 'Variable_costs_gas'] = (
-          variable_gas_cost_year_0 * price_increase_peak_electricity
-      )
-    self._yearly_operational_costs.fillna(0, inplace=True)
-
-    return self._yearly_operational_costs
-
-  def calculate_total_operational_incomes(self, retrofitting_scenario):
-    """
-    Calculate total operational incomes
-    :return: pd.DataFrame
-    """
-    building = self._building
-    if cte.YEAR not in building.onsite_electrical_production:
-      onsite_electricity_production = 0
-    else:
-      if retrofitting_scenario == 0 or retrofitting_scenario == 1:
-        onsite_electricity_production = 0
-      else:
-        onsite_electricity_production = building.onsite_electrical_production[cte.YEAR][0]/1000
-
-    for year in range(1, self._number_of_years + 1):
-      price_increase_electricity = math.pow(1 + self._electricity_price_index, year)
-      # todo: check the adequate assignation of price. Pilar
-      price_export = 0.075  # archetype.income.electricity_export
-      self._yearly_operational_incomes.loc[year, 'Incomes electricity'] = (
-          onsite_electricity_production * price_export * price_increase_electricity
-      )
-
-    self._yearly_operational_incomes.fillna(0, inplace=True)
-    return self._yearly_operational_incomes
-
-  def calculate_total_maintenance_costs(self):
-    """
-    Calculate total maintenance costs
-    :return: pd.DataFrame
-    """
-    building = self._building
-    archetype = self._archetype
-    # todo: change area pv when the variable exists
-    roof_area = 0
-    for roof in building.roofs:
-      roof_area += roof.solid_polygon.area
-    surface_pv = roof_area * 0.5
-
-    peak_heating = building.heating_peak_load[cte.YEAR][0]/1000
-    peak_cooling = building.heating_peak_load[cte.YEAR][0]/1000
-
-    maintenance_heating_0 = peak_heating * archetype.operational_cost.maintenance_heating
-    maintenance_cooling_0 = peak_cooling * archetype.operational_cost.maintenance_cooling
-    maintenance_pv_0 = surface_pv * archetype.operational_cost.maintenance_pv
-
-    for year in range(1, self._number_of_years + 1):
-      costs_increase = math.pow(1 + self._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, 'PV_maintenance'] = (
-          maintenance_pv_0 * costs_increase
-      )
-    self._yearly_maintenance_costs.fillna(0, inplace=True)
-    return self._yearly_maintenance_costs

costs/outputs/.gitignore

@@ -1,4 +0,0 @@
-# Ignore everything in this directory
-*
-# Except this file
-!.gitignore

costs/printing_results.py

@@ -1,58 +0,0 @@
-import plotly.graph_objects as go
-import matplotlib.pyplot as plt
-import plotly.express as px
-
-
-def printing_results(investmentcosts, life_cycle_results,total_floor_area):
-
-  labels = investmentcosts.index
-  values = investmentcosts['retrofitting_scenario_1']
-  values2 = investmentcosts['retrofitting_scenario_2']
-  values3 = investmentcosts['retrofitting_scenario_3']
-
-  fig = go.Figure(data=[go.Pie(labels=labels, values=values)])
-  fig2 = go.Figure(data=[go.Pie(labels=labels, values=values2)])
-  fig3 = go.Figure(data=[go.Pie(labels=labels, values=values3)])
-  # Set the layout properties
-  fig.update_layout(
-    title='Retrofitting scenario 1',
-    showlegend=True
-  )
-  fig2.update_layout(
-    title='Retrofitting scenario 2',
-    showlegend=True
-  )
-  fig3.update_layout(
-    title='Retrofitting scenario 3',
-    showlegend=True
-  )
-
-  # Display the chart
-  fig.show()
-  fig2.show()
-  fig3.show()
-
-  df = life_cycle_results / total_floor_area
-
-  # Transpose the DataFrame (swap columns and rows)
-  df_swapped = df.transpose()
-
-  # Reset the index to make the current index a regular column
-  df_swapped = df_swapped.reset_index()
-
-  # Assign new column names
-  df_swapped.columns = ['Scenarios', 'total_capital_costs_skin',
-                        'total_capital_costs_systems',
-                        'end_of_life_costs',
-                        'total_operational_costs',
-                        'total_maintenance_costs',
-                        'operational_incomes',
-                        'capital_incomes']
-
-  df_swapped.index = df_swapped['Scenarios']
-  df_swapped = df_swapped.drop('Scenarios', axis=1)
-  print(df_swapped)
-  fig = px.bar(df_swapped, title='Life Cycle Costs for buildings')
-  fig.show()
-  # Display the chart
-  plt.show()

costs/tmp/.gitignore

@@ -1,4 +0,0 @@
-# Ignore everything in this directory
-*
-# Except this file
-!.gitignore

costs/total_maintenance_costs.py

@@ -0,0 +1,61 @@
+"""
+Total maintenance costs module
+"""
+import math
+import pandas as pd
+from hub.city_model_structure.building import Building
+import hub.helpers.constants as cte
+
+from costs.configuration import Configuration
+from costs.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',
+        '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
+    for roof in building.roofs:
+      roof_area += roof.solid_polygon.area
+    surface_pv = roof_area * 0.5
+
+    peak_heating = building.heating_peak_load[cte.YEAR][0]
+    peak_cooling = building.cooling_peak_load[cte.YEAR][0]
+
+    maintenance_heating_0 = peak_heating * archetype.operational_cost.maintenance_heating
+    maintenance_cooling_0 = peak_cooling * archetype.operational_cost.maintenance_cooling
+    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, 'PV_maintenance'] = (
+          maintenance_pv_0 * costs_increase
+      )
+    self._yearly_maintenance_costs.fillna(0, inplace=True)
+    return self._yearly_maintenance_costs

costs/total_operational_costs.py

@@ -0,0 +1,92 @@
+"""
+Total operational costs module
+"""
+import math
+import pandas as pd
+
+from hub.city_model_structure.building import Building
+import hub.helpers.constants as cte
+
+from costs.configuration import Configuration
+from costs.cost_base import CostBase
+
+
+class TotalOperationalCosts(CostBase):
+  """
+  End of life costs class
+  """
+  def __init__(self, building: Building, configuration: Configuration):
+    super().__init__(building, configuration)
+    self._yearly_operational_costs = pd.DataFrame(
+      index=self._rng,
+      columns=[
+        'Fixed_costs_electricity_peak',
+        'Fixed_costs_electricity_monthly',
+        'Variable_costs_electricity',
+        'Fixed_costs_gas',
+        'Variable_costs_gas'
+      ],
+      dtype='float'
+    )
+
+  def calculate(self) -> pd.DataFrame:
+    """
+    Calculate total operational costs
+    :return: pd.DataFrame
+    """
+    building = self._building
+    archetype = self._archetype
+    total_floor_area = self._total_floor_area
+    factor_residential = total_floor_area / 80
+    # todo: split the heating between fuels
+    fixed_gas_cost_year_0 = 0
+    variable_gas_cost_year_0 = 0
+    electricity_heating = 0
+    domestic_hot_water_electricity = 0
+    if self._configuration.fuel_type == 1:
+      fixed_gas_cost_year_0 = archetype.operational_cost.fuels[1].fixed_monthly * 12 * factor_residential
+      variable_gas_cost_year_0 = (
+          (building.heating_consumption[cte.YEAR][0] + building.domestic_hot_water_consumption[cte.YEAR][0]) / 1000 *
+          archetype.operational_cost.fuels[1].variable[0]
+      )
+    if self._configuration.fuel_type == 0:
+      electricity_heating = building.heating_consumption[cte.YEAR][0] / 1000
+      domestic_hot_water_electricity = building.domestic_hot_water_consumption[cte.YEAR][0] / 1000
+
+    electricity_cooling = building.cooling_consumption[cte.YEAR][0] / 1000
+    electricity_lighting = building.lighting_electrical_demand[cte.YEAR]['insel meb'] / 1000
+    electricity_plug_loads = building.appliances_electrical_demand[cte.YEAR]['insel meb'] / 1000
+    electricity_distribution = 0
+    total_electricity_consumption = (
+        electricity_heating + electricity_cooling + electricity_lighting + domestic_hot_water_electricity +
+        electricity_plug_loads + electricity_distribution
+    )
+
+    # todo: change when peak electricity demand is coded. Careful with factor residential
+    peak_electricity_demand = 100  # self._peak_electricity_demand
+    variable_electricity_cost_year_0 = total_electricity_consumption * archetype.operational_cost.fuels[0].variable[0]
+    peak_electricity_cost_year_0 = peak_electricity_demand * archetype.operational_cost.fuels[0].fixed_power * 12
+    monthly_electricity_cost_year_0 = archetype.operational_cost.fuels[0].fixed_monthly * 12 * factor_residential
+
+    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)
+      price_increase_gas = math.pow(1 + self._configuration.gas_price_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
+      )
+      self._yearly_operational_costs.at[year, 'Variable_costs_electricity'] = (
+        float(variable_electricity_cost_year_0.iloc[0] * price_increase_electricity)
+      )
+      self._yearly_operational_costs.at[year, 'Fixed_costs_gas'] = fixed_gas_cost_year_0 * price_increase_gas
+      self._yearly_operational_costs.at[year, 'Variable_costs_gas'] = (
+          variable_gas_cost_year_0 * price_increase_peak_electricity
+      )
+      self._yearly_operational_costs.at[year, 'Variable_costs_gas'] = (
+          variable_gas_cost_year_0 * price_increase_peak_electricity
+      )
+    self._yearly_operational_costs.fillna(0, inplace=True)
+    return self._yearly_operational_costs

costs/total_operational_incomes.py

@@ -0,0 +1,41 @@
+"""
+Total operational incomes module
+"""
+import math
+import pandas as pd
+from hub.city_model_structure.building import Building
+import hub.helpers.constants as cte
+
+from costs.configuration import Configuration
+from costs.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
+    if cte.YEAR not in building.onsite_electrical_production:
+      onsite_electricity_production = 0
+    else:
+      onsite_electricity_production = building.onsite_electrical_production[cte.YEAR][0] / 1000
+
+    for year in range(1, self._configuration.number_of_years + 1):
+      price_increase_electricity = math.pow(1 + self._configuration.electricity_price_index, year)
+      # todo: check the adequate assignation of price. Pilar
+      price_export = 0.075  # archetype.income.electricity_export
+      self._yearly_operational_incomes.loc[year, 'Incomes electricity'] = (
+          onsite_electricity_production * price_export * price_increase_electricity
+      )
+
+    self._yearly_operational_incomes.fillna(0, inplace=True)
+    return self._yearly_operational_incomes

costs/version.py

@@ -0,0 +1,4 @@
+"""
+Cost version number
+"""
+__version__ = '0.1.0.0'

input_files/summerschool_one_building.geojson

@@ -1,294 +0,0 @@
-{
-  "type": "FeatureCollection",
-  "features": [
-    {
-      "type": "Feature",
-      "id": 12,
-      "geometry": {
-        "type": "Polygon",
-        "coordinates": [
-          [
-            [
-              -73.57945149010348,
-              45.49793915473101
-            ],
-            [
-              -73.57945502047383,
-              45.497935600591106
-            ],
-            [
-              -73.57945748913181,
-              45.49793681276347
-            ],
-            [
-              -73.57945995778985,
-              45.49793802493576
-            ],
-            [
-              -73.57946108986009,
-              45.49793688584562
-            ],
-            [
-              -73.57946222064952,
-              45.49793574585649
-            ],
-            [
-              -73.57946503164756,
-              45.497932909392325
-            ],
-            [
-              -73.5794800321942,
-              45.497917804072586
-            ],
-            [
-              -73.57949503273288,
-              45.49790269875081
-            ],
-            [
-              -73.57950823165471,
-              45.49788939886833
-            ],
-            [
-              -73.57952143057031,
-              45.497876098984314
-            ],
-            [
-              -73.57952481016481,
-              45.49787269972034
-            ],
-            [
-              -73.57952818975889,
-              45.49786930045622
-            ],
-            [
-              -73.57963374256275,
-              45.49776298233438
-            ],
-            [
-              -73.57963739684415,
-              45.497759299424665
-            ],
-            [
-              -73.57956562282082,
-              45.49772405755894
-            ],
-            [
-              -73.5795624921933,
-              45.497722521006246
-            ],
-            [
-              -73.57955974509859,
-              45.4977252944393
-            ],
-            [
-              -73.57953557695755,
-              45.497749634054365
-            ],
-            [
-              -73.5795114087957,
-              45.497773973664174
-            ],
-            [
-              -73.57945076790263,
-              45.49783505227953
-            ],
-            [
-              -73.57939012687844,
-              45.49789613086214
-            ],
-            [
-              -73.57938759058709,
-              45.49789868818189
-            ],
-            [
-              -73.57938505429556,
-              45.49790124550157
-            ],
-            [
-              -73.57941717242674,
-              45.49791701633786
-            ],
-            [
-              -73.5794136407655,
-              45.497920563278754
-            ],
-            [
-              -73.57943256542505,
-              45.497929854507255
-            ],
-            [
-              -73.57944202776348,
-              45.49793450461953
-            ],
-            [
-              -73.57945149010348,
-              45.49793915473101
-            ]
-          ]
-        ]
-      },
-      "properties": {
-        "OBJECTID_12": 12,
-        "gml_id": 1340982,
-        "gml_parent": "fme-gen-5fa2a82b-c38e-4bf0-9e8f-10a47b9f64f7",
-        "citygml_ta": "http://www.opengis.net/citygml/building/2.0",
-        "citygml_fe": "cityObjectMember",
-        "citygml__1": " ",
-        "citygml__2": " ",
-        "gml_descri": " ",
-        "gml_name": " ",
-        "citygml_cr": " ",
-        "citygml_te": " ",
-        "externalRe": " ",
-        "external_1": " ",
-        "external_2": " ",
-        "citygml_ge": " ",
-        "citygml_re": " ",
-        "citygml__3": " ",
-        "citygml_ap": " ",
-        "citygml_cl": " ",
-        "citygml__4": " ",
-        "citygml_fu": " ",
-        "citygml__5": " ",
-        "citygml_us": " ",
-        "citygml__6": " ",
-        "citygml_ye": " ",
-        "citygml__7": " ",
-        "citygml_ro": " ",
-        "citygml__8": " ",
-        "citygml_me": 19.113,
-        "citygml__9": "#m",
-        "citygml_st": " ",
-        "citygml_10": " ",
-        "citygml_11": " ",
-        "citygml_12": " ",
-        "citygml_13": " ",
-        "citygml_14": " ",
-        "citygml_ou": " ",
-        "citygml_in": " ",
-        "citygml_bo": " ",
-        "citygml_le": " ",
-        "citygml_15": " ",
-        "citygml_co": " ",
-        "citygml_ad": " ",
-        "Volume": "2931.350",
-        "parcelle": " ",
-        "OBJECTID": 1056,
-        "gml_id_1": "384b2b1c-2e25-4f6a-b082-d272dba3453f",
-        "gml_pare_1": 1340982,
-        "citygml_16": "http://www.opengis.net/citygml/building/2.0",
-        "citygml_17": "boundedBy",
-        "citygml_18": " ",
-        "citygml_19": " ",
-        "gml_desc_1": " ",
-        "gml_name_1": " ",
-        "citygml_20": " ",
-        "citygml_21": " ",
-        "external_3": " ",
-        "external_4": " ",
-        "external_5": " ",
-        "citygml_22": " ",
-        "citygml_23": " ",
-        "citygml_24": " ",
-        "citygml_25": " ",
-        "citygml_26": " ",
-        "citygml_op": " ",
-        "Area": 191.404,
-        "FID_": 0,
-        "Join_Count": 2,
-        "TARGET_FID": 1058,
-        "gml_id_12": 1340982,
-        "gml_pare_2": "fme-gen-5fa2a82b-c38e-4bf0-9e8f-10a47b9f64f7",
-        "citygml_27": "http://www.opengis.net/citygml/building/2.0",
-        "citygml_28": "cityObjectMember",
-        "citygml_29": " ",
-        "citygml_30": " ",
-        "gml_desc_2": " ",
-        "gml_name_2": " ",
-        "citygml_31": " ",
-        "citygml_32": " ",
-        "external_6": " ",
-        "external_7": " ",
-        "external_8": " ",
-        "citygml_33": " ",
-        "citygml_34": " ",
-        "citygml_35": " ",
-        "citygml_36": " ",
-        "citygml_37": " ",
-        "citygml_38": " ",
-        "citygml_39": " ",
-        "citygml_40": " ",
-        "citygml_41": " ",
-        "citygml_42": " ",
-        "citygml_43": " ",
-        "citygml_44": " ",
-        "citygml_45": " ",
-        "citygml_46": " ",
-        "citygml_47": 19.113,
-        "citygml_48": "#m",
-        "citygml_49": " ",
-        "citygml_50": " ",
-        "citygml_51": " ",
-        "citygml_52": " ",
-        "citygml_53": " ",
-        "citygml_54": " ",
-        "citygml_55": " ",
-        "citygml_56": " ",
-        "citygml_57": " ",
-        "citygml_58": " ",
-        "citygml_59": " ",
-        "citygml_60": " ",
-        "citygml_61": " ",
-        "Volume_1": "2931.350",
-        "Field": 0,
-        "Field1": 0,
-        "OBJECTID_1": 1056,
-        "gml_id_12_": "384b2b1c-2e25-4f6a-b082-d272dba3453f",
-        "gml_pare_3": 1340982,
-        "citygml_62": "http://www.opengis.net/citygml/building/2.0",
-        "citygml_63": "boundedBy",
-        "citygml_64": " ",
-        "citygml_65": " ",
-        "gml_desc_3": " ",
-        "gml_name_3": " ",
-        "citygml_66": " ",
-        "citygml_67": " ",
-        "external_9": " ",
-        "externa_10": " ",
-        "externa_11": " ",
-        "citygml_68": " ",
-        "citygml_69": " ",
-        "citygml_70": " ",
-        "citygml_71": " ",
-        "citygml_72": " ",
-        "citygml_73": " ",
-        "Area_1": 191.404,
-        "cityGML_hi": 0,
-        "Z_Min": 46.1162,
-        "Z_Max": 64.399,
-        "Shape_Leng": 63.6906066955,
-        "ID_UEV": "01036804",
-        "CIVIQUE_DE": " 2170",
-        "CIVIQUE_FI": " 2170",
-        "NOM_RUE": "rue Bishop  (MTL)",
-        "MUNICIPALI": 50,
-        "ETAGE_HORS": 3,
-        "NOMBRE_LOG": 1,
-        "ANNEE_CONS": 1900,
-        "CODE_UTILI": 6000,
-        "LIBELLE_UT": "Immeuble à bureaux",
-        "CATEGORIE_": "Régulier",
-        "MATRICULE8": "9839-57-7770-3-000-0000",
-        "SUPERFICIE": 259,
-        "SUPERFIC_1": 490,
-        "NO_ARROND_": "REM19",
-        "Shape_Le_1": 0.00093336765858,
-        "Shape_Ar_1": 3.0845126501e-8,
-        "Z_Min_1": null,
-        "Z_Max_1": null,
-        "Shape_Length": 63.69060669550123,
-        "Shape_Area": 174.69050030775531
-      }
-    }
-  ]
-}

out_files/.gitignore

@@ -1,4 +0,0 @@
-# Ignore everything in this directory
-.gitignore
-# Except this file
-!.gitignore

pyproject.toml

@@ -0,0 +1,8 @@
+# pyproject.toml
+
+[build-system]
+requires = ["setuptools>=61.0.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[options.packages.find_namespace]
+where = "costs"

requirements.txt

@@ -1,2 +1,3 @@
 numpy_financial
 cerc_hub
+pandas

setup.py

@@ -0,0 +1,36 @@
+import glob
+import pathlib
+from distutils.util import convert_path
+from setuptools import setup
+
+with pathlib.Path('requirements.txt').open() as r:
+  install_requires = [
+    str(requirement).replace('\n', '')
+    for requirement
+    in r.readlines()
+  ]
+install_requires.append('setuptools')
+
+main_ns = {}
+version = convert_path('costs/version.py')
+with open(version) as f:
+  exec(f.read(), main_ns)
+
+setup(
+  name='cerc-costs',
+  version=main_ns['__version__'],
+  description="CERC costs contains the basic cost calculation per CERC-Hub building",
+  long_description="CERC costs contains the basic cost calculation per CERC-Hub building",
+  classifiers=[
+    "License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3",
+  ],
+  include_package_data=True,
+  packages=['costs'],
+  setup_requires=install_requires,
+  install_requires=install_requires,
+  data_files=[
+    ('costs', glob.glob('requirements.txt'))
+  ]
+)

tests/output/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

tests/unit_tests.py

@@ -0,0 +1,73 @@
+import glob
+import os
+import subprocess
+import unittest
+
+from pathlib import Path
+
+from hub.exports.energy_building_exports_factory import EnergyBuildingsExportsFactory
+from hub.exports.exports_factory import ExportsFactory
+from hub.imports.construction_factory import ConstructionFactory
+from hub.imports.energy_systems_factory import EnergySystemsFactory
+from hub.imports.geometry_factory import GeometryFactory
+from hub.imports.results_factory import ResultFactory
+from hub.imports.usage_factory import UsageFactory
+from hub.helpers.dictionaries import Dictionaries
+
+from costs.cost import Cost
+from costs.constants import SKIN_RETROFIT, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT_AND_PV
+
+
+class UnitTests(unittest.TestCase):
+  def setUp(self) -> None:
+    city_file = Path("./tests/data/test.geojson").resolve()
+    output_path = Path('./tests/output/').resolve()
+    city = GeometryFactory('geojson',
+                           city_file,
+                           height_field='citygml_me',
+                           year_of_construction_field='ANNEE_CONS',
+                           function_field='CODE_UTILI',
+                           function_to_hub=Dictionaries().montreal_function_to_hub_function).city
+    ConstructionFactory('nrcan', city).enrich()
+    UsageFactory('nrcan', city).enrich()
+    ExportsFactory('sra', city, output_path).export()
+    sra_file = str((output_path / f'{city.name}_sra.xml').resolve())
+    subprocess.run(['/usr/local/bin/sra', sra_file])
+    ResultFactory('sra', city, output_path).enrich()
+
+    for building in city.buildings:
+      building.energy_systems_archetype_name = 'system 1 gas pv'
+    EnergySystemsFactory('montreal_custom', city).enrich()
+    EnergyBuildingsExportsFactory('insel_monthly_energy_balance', city, output_path).export()
+    _insel_files = glob.glob(f'{output_path}/*.insel')
+    for insel_file in _insel_files:
+      subprocess.run(['insel', str(insel_file)], stdout=subprocess.DEVNULL)
+    ResultFactory('insel_monthly_energy_balance', city, output_path).enrich()
+    self._city = city
+
+  def test_current_status(self):
+    for building in self._city.buildings:
+      result = Cost(building).life_cycle
+      self.assertIsNotNone(result)
+      self.assertEqual(0, result.values[0])
+
+  def test_scenario_1(self):
+    for building in self._city.buildings:
+      result = Cost(building, retrofit_scenario=SKIN_RETROFIT).life_cycle
+      self.assertIsNotNone(result)
+
+  def test_scenario_2(self):
+    for building in self._city.buildings:
+      result = Cost(building, retrofit_scenario=SYSTEM_RETROFIT_AND_PV).life_cycle
+      self.assertIsNotNone(result)
+      self.assertEqual(0, result.values[0])
+
+  def test_scenario_3(self):
+    for building in self._city.buildings:
+      result = Cost(building, retrofit_scenario=SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV).life_cycle
+      self.assertIsNotNone(result)
+
+  def tearDown(self):
+    files = glob.glob('output/[!.]*')
+    for file in files:
+      os.unlink(file)