cleaned code. not found bug. not working

costs/__init__.py

@@ -36,6 +36,14 @@ RETROFITTING_SCENARIOS = [
   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}/*')

costs/__main__.py

@@ -9,7 +9,6 @@ from pathlib import Path
 
 import numpy_financial as npf
 import pandas as pd
-from energy_systems_sizing import EnergySystemsSizing
 from hub.catalog_factories.costs_catalog_factory import CostCatalogFactory
 from hub.helpers.dictionaries import Dictionaries
 from hub.imports.construction_factory import ConstructionFactory
@@ -19,18 +18,18 @@ 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
 
-# import constants
+from costs import CONSTRUCTION_FORMAT
-from costs import CLIMATE_REFERENCE_CITY, WEATHER_FILE, WEATHER_FORMAT, CONSTRUCTION_FORMAT, USAGE_FORMAT
+from costs import ENERGY_SYSTEM_FORMAT, RETROFITTING_SCENARIOS, NUMBER_OF_YEARS
-from costs import ENERGY_SYSTEM_FORMAT, ATTIC_HEATED_CASE, BASEMENT_HEATED_CASE, 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 costs import file_path, tmp_folder, out_path
+from results import Results
 
 
 def _npv_from_list(npv_discount_rate, list_cashflow):
@@ -47,36 +46,44 @@ def _search_archetype(costs_catalog, building_function):
 
 
 life_cycle_results = pd.DataFrame()
-print('[city creation start]')
+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='heightmax',
+                       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_reference_city = climate_reference_city
-city.climate_file = (tmp_folder / f'{CLIMATE_REFERENCE_CITY}.cli').resolve()
+city.climate_file = (tmp_folder / f'{climate_reference_city}.cli').resolve()
 print(f'city created from {file_path}')
-WeatherFactory(WEATHER_FORMAT, city, file_name=WEATHER_FILE).enrich()
+WeatherFactory(weather_format, city).enrich()
 print('enrich weather... done')
-ConstructionFactory(CONSTRUCTION_FORMAT, city).enrich()
+ConstructionFactory(construction_format, city).enrich()
 print('enrich constructions... done')
-UsageFactory(USAGE_FORMAT, city).enrich()
+UsageFactory(usage_format, city).enrich()
 print('enrich usage... done')
 for building in city.buildings:
-  building.energy_systems_archetype_name = 'system 1 gas'
+  building.energy_systems_archetype_name = 'system 1 gas pv'
-EnergySystemsFactory(ENERGY_SYSTEM_FORMAT, city).enrich()
+EnergySystemsFactory(energy_systems_format, city).enrich()
 print('enrich systems... done')
-print('exporting:')
-catalog = CostCatalogFactory('montreal_custom').catalog
-print('costs catalog access... done')
-sra_file = (tmp_folder / f'{city.name}_sra.xml').resolve()
-SraEngine(city, sra_file, tmp_folder, WEATHER_FILE)
-print('    sra processed...')
 
-for building in city.buildings:
+print('exporting:')
-  building.attic_heated = ATTIC_HEATED_CASE
+sra_file = (tmp_folder / f'{city.name}_sra.xml').resolve()
-  building.basement_heated = BASEMENT_HEATED_CASE
+SraEngine(city, sra_file, tmp_folder)
+print('    sra processed...')
+catalog = CostCatalogFactory('montreal_custom').catalog
 
 for retrofitting_scenario in RETROFITTING_SCENARIOS:
 
@@ -93,13 +100,26 @@ for retrofitting_scenario in RETROFITTING_SCENARIOS:
     print('enrich systems... done')
 
   MonthlyEnergyBalanceEngine(city, tmp_folder)
+  print('    insel processed...')
 
-  EnergySystemsSizing(city).enrich()
+  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')
@@ -114,7 +134,7 @@ for retrofitting_scenario in RETROFITTING_SCENARIOS:
     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()
+    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')
@@ -124,6 +144,33 @@ for retrofitting_scenario in RETROFITTING_SCENARIOS:
       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']
@@ -175,14 +222,14 @@ for retrofitting_scenario in RETROFITTING_SCENARIOS:
         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_operational_incomes,
-                                                               life_cycle_capital_incomes]
+                                                               -life_cycle_capital_incomes]
 
     life_cycle_results.index = ['total_capital_costs_skin',
                                 'total_capital_costs_systems',
@@ -192,5 +239,16 @@ for retrofitting_scenario in RETROFITTING_SCENARIOS:
                                 'operational_incomes',
                                 'capital_incomes']
 
-    print(life_cycle_results)
     print(f'Scenario {retrofitting_scenario} {life_cycle_costs}')
+
+#    printing_results(investmentcosts, life_cycle_results, total_floor_area)
+
+
+
+
+
+
+
+
+
+

costs/__main__emissions.py

@@ -0,0 +1,68 @@
+"""
+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/life_cycle_costs.py

@@ -102,9 +102,8 @@ class LifeCycleCosts:
             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_heating = building.heating_peak_load[cte.YEAR].values[0]/1000
+    peak_cooling = building.cooling_peak_load[cte.YEAR][0]/1000
-    peak_cooling = building.cooling_peak_load[cte.YEAR].values[0]/1000
     # todo: change area pv when the variable exists
     roof_area = 0
     for roof in building.roofs:
@@ -247,6 +246,9 @@ class LifeCycleCosts:
     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):
     """
@@ -280,9 +282,10 @@ class LifeCycleCosts:
         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 = 100  # self._peak_electricity_demand
+    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
@@ -312,7 +315,7 @@ class LifeCycleCosts:
 
     return self._yearly_operational_costs
 
-  def calculate_total_operational_incomes(self):
+  def calculate_total_operational_incomes(self, retrofitting_scenario):
     """
     Calculate total operational incomes
     :return: pd.DataFrame
@@ -320,6 +323,9 @@ class LifeCycleCosts:
     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
 
@@ -347,8 +353,8 @@ class LifeCycleCosts:
       roof_area += roof.solid_polygon.area
     surface_pv = roof_area * 0.5
 
-    peak_heating = building.heating_peak_load[cte.YEAR][cte.HEATING_PEAK_LOAD][0]
+    peak_heating = building.heating_peak_load[cte.YEAR][0]/1000
-    peak_cooling = building.cooling_peak_load[cte.YEAR][cte.COOLING_PEAK_LOAD][0]
+    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

costs/printing_results.py

@@ -0,0 +1,58 @@
+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()

input_files/summerschool_one_building.geojson

@@ -0,0 +1,294 @@
+{
+  "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
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+              -73.57950823165471,
+              45.49788939886833
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+              -73.57952143057031,
+              45.497876098984314
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+              -73.57952481016481,
+              45.49787269972034
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+              -73.57952818975889,
+              45.49786930045622
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+            [
+              -73.57963374256275,
+              45.49776298233438
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+              -73.57963739684415,
+              45.497759299424665
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+              -73.57956562282082,
+              45.49772405755894
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+              -73.5795624921933,
+              45.497722521006246
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+              -73.57955974509859,
+              45.4977252944393
+            ],
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+              -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": " ",
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+  ]
+}

out_files/.gitignore

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