Ongoing developments for the course workshop, including graphs implementation

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
@@ -19,10 +18,11 @@ 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
-
+import numpy as np
+from printing_results import *
+from hub.helpers import constants as cte
 from life_cycle_costs import LifeCycleCosts
 
-# import constants
 from costs import CLIMATE_REFERENCE_CITY, WEATHER_FILE, WEATHER_FORMAT, CONSTRUCTION_FORMAT, USAGE_FORMAT
 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
@@ -33,8 +33,7 @@ from costs import EMISSION_FACTOR_GAS_QUEBEC, EMISSION_FACTOR_ELECTRICITY_QUEBEC
   EMISSION_FACTOR_BIOMASS_QUEBEC, EMISSION_FACTOR_FUEL_OIL_QUEBEC, EMISSION_FACTOR_DIESEL_QUEBEC
 
 # import paths
-from costs import file_path, tmp_folder, out_path
-
+from results import Results
 
 def _npv_from_list(npv_discount_rate, list_cashflow):
   lcc_value = npf.npv(npv_discount_rate, list_cashflow)
@@ -50,36 +49,44 @@ 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_file = (tmp_folder / f'{CLIMATE_REFERENCE_CITY}.cli').resolve()
+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, 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'
-EnergySystemsFactory(ENERGY_SYSTEM_FORMAT, city).enrich()
+  building.energy_systems_archetype_name = 'system 1 gas pv'
+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:
-  building.attic_heated = ATTIC_HEATED_CASE
-  building.basement_heated = BASEMENT_HEATED_CASE
+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:
 
@@ -96,13 +103,26 @@ 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')
@@ -117,7 +137,7 @@ 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')
@@ -127,6 +147,31 @@ 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')
 
+    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']
@@ -178,14 +223,14 @@ 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_capital_incomes]
+                                                               -life_cycle_operational_incomes,
+                                                               -life_cycle_capital_incomes]
 
     life_cycle_results.index = ['total_capital_costs_skin',
                                 'total_capital_costs_systems',
@@ -195,5 +240,16 @@ 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/life_cycle_costs.py

@@ -102,9 +102,9 @@ class LifeCycleCosts:
             surface_transparent += thermal_boundary.opaque_area * thermal_boundary.window_ratio
 
     chapters = archetype.capital_cost
-
-    peak_heating = building.heating_peak_load[cte.YEAR].values[0]/1000
-    peak_cooling = building.cooling_peak_load[cte.YEAR].values[0]/1000
+    print('kk')
+    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:
@@ -247,6 +247,9 @@ 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):
     """
@@ -283,7 +286,7 @@ class LifeCycleCosts:
     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
@@ -313,7 +316,7 @@ 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
@@ -322,7 +325,10 @@ class LifeCycleCosts:
     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
+      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)
@@ -348,8 +354,8 @@ 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_cooling = building.cooling_peak_load[cte.YEAR][cte.COOLING_PEAK_LOAD][0]
+    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

costs/printing_results.py

@@ -0,0 +1,60 @@
+import numpy as np
+
+import plotly.graph_objects as go
+import plotly.offline as offline
+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 1',
+    showlegend=True
+  )
+  fig3.update_layout(
+    title='Retrofitting scenario 1',
+    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
+            ],
+            [
+              -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
+      }
+    }
+  ]
+}