Merge pull request 'report' (#13) from report into main

Reviewed-on: https://nextgenerations-cities.encs.concordia.ca/gitea/s_ranjbar/energy_system_modelling_workflow/pulls/13

energy_system_retrofit.py

@@ -1,4 +1,3 @@
-from scripts.geojson_creator import process_geojson
 from pathlib import Path
 import subprocess
 from scripts.ep_run_enrich import energy_plus_workflow
@@ -8,57 +7,92 @@ from hub.imports.construction_factory import ConstructionFactory
 from hub.imports.usage_factory import UsageFactory
 from hub.imports.weather_factory import WeatherFactory
 from hub.imports.results_factory import ResultFactory
-from scripts.energy_system_analysis_report import EnergySystemAnalysisReport
+from scripts.energy_system_retrofit_report import EnergySystemRetrofitReport
+from scripts.geojson_creator import process_geojson
 from scripts import random_assignation
 from hub.imports.energy_systems_factory import EnergySystemsFactory
 from scripts.energy_system_sizing import SystemSizing
-from scripts.energy_system_retrofit_results import system_results, new_system_results
+from scripts.solar_angles import CitySolarAngles
+from scripts.pv_sizing_and_simulation import PVSizingSimulation
+from scripts.energy_system_retrofit_results import consumption_data, cost_data
 from scripts.energy_system_sizing_and_simulation_factory import EnergySystemsSimulationFactory
 from scripts.costs.cost import Cost
-from scripts.costs.constants import SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT_AND_PV
+from scripts.costs.constants import SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT_AND_PV, CURRENT_STATUS
 import hub.helpers.constants as cte
 from hub.exports.exports_factory import ExportsFactory
+from scripts.pv_feasibility import pv_feasibility
+
 # Specify the GeoJSON file path
+input_files_path = (Path(__file__).parent / 'input_files')
+input_files_path.mkdir(parents=True, exist_ok=True)
 geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.0001)
-file_path = (Path(__file__).parent / 'input_files' / 'output_buildings.geojson')
-# Specify the output path for the PDF file
+geojson_file_path = input_files_path / 'output_buildings.geojson'
 output_path = (Path(__file__).parent / 'out_files').resolve()
-# Create city object from GeoJSON file
-city = GeometryFactory('geojson',
-                       path=file_path,
+output_path.mkdir(parents=True, exist_ok=True)
+energy_plus_output_path = output_path / 'energy_plus_outputs'
+energy_plus_output_path.mkdir(parents=True, exist_ok=True)
+simulation_results_path = (Path(__file__).parent / 'out_files' / 'simulation_results').resolve()
+simulation_results_path.mkdir(parents=True, exist_ok=True)
+sra_output_path = output_path / 'sra_outputs'
+sra_output_path.mkdir(parents=True, exist_ok=True)
+cost_analysis_output_path = output_path / 'cost_analysis'
+cost_analysis_output_path.mkdir(parents=True, exist_ok=True)
+city = GeometryFactory(file_type='geojson',
+                       path=geojson_file_path,
                        height_field='height',
                        year_of_construction_field='year_of_construction',
                        function_field='function',
                        function_to_hub=Dictionaries().montreal_function_to_hub_function).city
-# Enrich city data
 ConstructionFactory('nrcan', city).enrich()
-
 UsageFactory('nrcan', city).enrich()
 WeatherFactory('epw', city).enrich()
-ExportsFactory('sra', city, output_path).export()
-sra_path = (output_path / f'{city.name}_sra.xml').resolve()
+ExportsFactory('sra', city, sra_output_path).export()
+sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
 subprocess.run(['sra', str(sra_path)])
-ResultFactory('sra', city, output_path).enrich()
-energy_plus_workflow(city)
+ResultFactory('sra', city, sra_output_path).enrich()
+pv_feasibility(-73.5681295982132, 45.49218262677643, 0.0001, selected_buildings=city.buildings)
+energy_plus_workflow(city, energy_plus_output_path)
+solar_angles = CitySolarAngles(city.name,
+                               city.latitude,
+                               city.longitude,
+                               tilt_angle=45,
+                               surface_azimuth_angle=180).calculate
 random_assignation.call_random(city.buildings, random_assignation.residential_systems_percentage)
 EnergySystemsFactory('montreal_custom', city).enrich()
 SystemSizing(city.buildings).montreal_custom()
-current_system = new_system_results(city.buildings)
+current_status_energy_consumption = consumption_data(city)
+current_status_life_cycle_cost = {}
+for building in city.buildings:
+  cost_retrofit_scenario = CURRENT_STATUS
+  lcc_dataframe = Cost(building=building,
+                       retrofit_scenario=cost_retrofit_scenario,
+                       fuel_tariffs=['Electricity-D', 'Gas-Energir']).life_cycle
+  lcc_dataframe.to_csv(cost_analysis_output_path / f'{building.name}_current_status_lcc.csv')
+  current_status_life_cycle_cost[f'{building.name}'] = cost_data(building, lcc_dataframe, cost_retrofit_scenario)
 random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
 EnergySystemsFactory('montreal_future', city).enrich()
 for building in city.buildings:
-  EnergySystemsSimulationFactory('archetype1', building=building, output_path=output_path).enrich()
-  print(building.energy_consumption_breakdown[cte.ELECTRICITY][cte.COOLING] +
-        building.energy_consumption_breakdown[cte.ELECTRICITY][cte.HEATING] +
-        building.energy_consumption_breakdown[cte.ELECTRICITY][cte.DOMESTIC_HOT_WATER])
-new_system = new_system_results(city.buildings)
-# EnergySystemAnalysisReport(city, output_path).create_report(current_system, new_system)
+  if 'PV' in building.energy_systems_archetype_name:
+    ghi = [x / cte.WATTS_HOUR_TO_JULES for x in building.roofs[0].global_irradiance[cte.HOUR]]
+    pv_sizing_simulation = PVSizingSimulation(building,
+                                              solar_angles,
+                                              tilt_angle=45,
+                                              module_height=1,
+                                              module_width=2,
+                                              ghi=ghi)
+    pv_sizing_simulation.pv_output()
+  if building.energy_systems_archetype_name == 'PV+4Pipe+DHW':
+    EnergySystemsSimulationFactory('archetype13', building=building, output_path=simulation_results_path).enrich()
+retrofitted_energy_consumption = consumption_data(city)
+retrofitted_life_cycle_cost = {}
 for building in city.buildings:
-  costs = Cost(building=building, retrofit_scenario=SYSTEM_RETROFIT_AND_PV).life_cycle
-  costs.to_csv(output_path / f'{building.name}_lcc.csv')
-  (costs.loc['global_operational_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].
-   to_csv(output_path / f'{building.name}_op.csv'))
-  costs.loc['global_capital_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].to_csv(
-    output_path / f'{building.name}_cc.csv')
-  costs.loc['global_maintenance_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].to_csv(
-    output_path / f'{building.name}_m.csv')
+  cost_retrofit_scenario = SYSTEM_RETROFIT_AND_PV
+  lcc_dataframe = Cost(building=building,
+                       retrofit_scenario=cost_retrofit_scenario,
+                       fuel_tariffs=['Electricity-D', 'Gas-Energir']).life_cycle
+  lcc_dataframe.to_csv(cost_analysis_output_path / f'{building.name}_retrofitted_lcc.csv')
+  retrofitted_life_cycle_cost[f'{building.name}'] = cost_data(building, lcc_dataframe, cost_retrofit_scenario)
+(EnergySystemRetrofitReport(city, output_path, 'PV Implementation and System Retrofit',
+                            current_status_energy_consumption, retrofitted_energy_consumption,
+                            current_status_life_cycle_cost, retrofitted_life_cycle_cost).create_report())
+

hub/catalog_factories/data_models/energy_systems/emission_system.py

@@ -10,7 +10,7 @@ class EmissionSystem:
   """
   Emission system class
   """
-  def __init__(self, system_id, model_name=None, system_type=None, parasitic_energy_consumption=None):
+  def __init__(self, system_id, model_name=None, system_type=None, parasitic_energy_consumption=0):
 
     self._system_id = system_id
     self._model_name = model_name

hub/catalog_factories/energy_systems/montreal_custom_catalog.py

@@ -135,7 +135,7 @@ class MontrealCustomCatalog(Catalog):
       equipment_id = float(equipment['@id'])
       equipment_type = equipment['@type']
       model_name = equipment['name']
-      parasitic_consumption = None
+      parasitic_consumption = 0
       if 'parasitic_consumption' in equipment:
         parasitic_consumption = float(equipment['parasitic_consumption']['#text']) / 100
 

hub/catalog_factories/energy_systems/montreal_future_system_catalogue.py

@@ -262,7 +262,7 @@ class MontrealFutureSystemCatalogue(Catalog):
         system_id = None
         model_name = None
         system_type = None
-        parasitic_energy_consumption = None
+        parasitic_energy_consumption = 0
         emission_system = EmissionSystem(system_id=system_id,
                                          model_name=model_name,
                                          system_type=system_type,

hub/city_model_structure/energy_systems/emission_system.py

@@ -13,7 +13,7 @@ class EmissionSystem:
   def __init__(self):
     self._model_name = None
     self._type = None
-    self._parasitic_energy_consumption = None
+    self._parasitic_energy_consumption = 0
 
   @property
   def model_name(self):

hub/data/costs/montreal_costs_completed.xml

@@ -187,7 +187,7 @@
 				<hvac cost_unit="%">1.5</hvac>
 				<photovoltaic cost_unit="%">3.6</photovoltaic>
 			</subsidies>
-            <electricity_export cost_unit="currency/kWh">0.07</electricity_export>
+            <electricity_export cost_unit="currency/kWh">0.075</electricity_export>
             <tax_reduction cost_unit="%">5</tax_reduction>
         </incomes>
     </archetype>

hub/imports/energy_systems/montreal_custom_energy_system_parameters.py

@@ -136,10 +136,14 @@ class MontrealCustomEnergySystemParameters:
       _distribution_system.distribution_consumption_variable_flow = \
         archetype_distribution_system.distribution_consumption_variable_flow
       _distribution_system.heat_losses = archetype_distribution_system.heat_losses
-      _emission_system = None
+      _generic_emission_system = None
       if archetype_distribution_system.emission_systems is not None:
-        _emission_system = EmissionSystem()
-      _distribution_system.emission_systems = [_emission_system]
+        _emission_systems = []
+        for emission_system in archetype_distribution_system.emission_systems:
+          _generic_emission_system = EmissionSystem()
+          _generic_emission_system.parasitic_energy_consumption = emission_system.parasitic_energy_consumption
+          _emission_systems.append(_generic_emission_system)
+        _distribution_system.emission_systems = _emission_systems
       _distribution_systems.append(_distribution_system)
     return _distribution_systems
 

hub/imports/energy_systems/montreal_future_energy_systems_parameters.py

@@ -185,10 +185,14 @@ class MontrealFutureEnergySystemParameters:
         _distribution_system.distribution_consumption_variable_flow = \
           archetype_distribution_system.distribution_consumption_variable_flow
         _distribution_system.heat_losses = archetype_distribution_system.heat_losses
-        _emission_system = None
+        _generic_emission_system = None
         if archetype_distribution_system.emission_systems is not None:
-          _emission_system = EmissionSystem()
-        _distribution_system.emission_systems = [_emission_system]
+          _emission_systems = []
+          for emission_system in archetype_distribution_system.emission_systems:
+            _generic_emission_system = EmissionSystem()
+            _generic_emission_system.parasitic_energy_consumption = emission_system.parasitic_energy_consumption
+            _emission_systems.append(_generic_emission_system)
+          _distribution_system.emission_systems = _emission_systems
         _distribution_systems.append(_distribution_system)
       return _distribution_systems
 

input_files/output_buildings_expanded.geojson

@@ -0,0 +1,863 @@
+{
+  "type": "FeatureCollection",
+  "features": [
+    {
+      "type": "Feature",
+      "geometry": {
+        "type": "Polygon",
+        "coordinates": [
+          [
+            [
+              -73.56769087843276,
+              45.49251875903776
+            ],
+            [
+              -73.56765050367694,
+              45.492560280202284
+            ],
+            [
+              -73.5677794213865,
+              45.49262188364245
+            ],
+            [
+              -73.56781916241786,
+              45.49258006136105
+            ],
+            [
+              -73.56769087843276,
+              45.49251875903776
+            ]
+          ]
+        ]
+      },
+      "id": 173347,
+      "properties": {
+        "name": "01044617",
+        "address": "rue Victor-Hugo (MTL) 1666",
+        "function": "1000",
+        "height": 9,
+        "year_of_construction": 1986
+      }
+    },
+    {
+      "type": "Feature",
+      "geometry": {
+        "type": "Polygon",
+        "coordinates": [
+          [
+            [
+              -73.56765050367694,
+              45.492560280202284
+            ],
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+              45.49259744179384
+            ],
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+              -73.5676075694645,
+              45.49260454199484
+            ],
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+              45.49266394156485
+            ],
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+              -73.56773726906921,
+              45.49266624130272
+            ],
+            [
+              -73.5677794213865,
+              45.49262188364245
+            ],
+            [
+              -73.56765050367694,
+              45.492560280202284
+            ]
+          ]
+        ]
+      },
+      "id": 173348,
+      "properties": {
+        "name": "01044619",
+        "address": "rue Victor-Hugo (MTL) 1670",
+        "function": "1000",
+        "height": 9,
+        "year_of_construction": 1986
+      }
+    },
+    {
+      "type": "Feature",
+      "geometry": {
+        "type": "Polygon",
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+              45.492524742569145
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+              45.49262354174874
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+              45.492505541343576
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+              45.492284841271946
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+            ],
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+              45.49201434154299
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+              -73.56837326884313,
+              45.492163841620254
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+              45.492524742569145
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+          ]
+        ]
+      },
+      "id": 173403,
+      "properties": {
+        "name": "01044334",
+        "address": "rue Saint-Jacques (MTL) 1460",
+        "function": "1000",
+        "height": 15,
+        "year_of_construction": 1985
+      }
+    },
+    {
+      "type": "Feature",
+      "geometry": {
+        "type": "Polygon",
+        "coordinates": [
+          [
+            [
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+          ]
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+      "properties": {
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+        "address": "rue Victor-Hugo (MTL) 1600",
+        "function": "1000",
+        "height": 9,
+        "year_of_construction": 1986
+      }
+    },
+    {
+      "type": "Feature",
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+        "type": "Polygon",
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+          ]
+        ]
+      },
+      "id": 175785,
+      "properties": {
+        "name": "01044602",
+        "address": "rue Victor-Hugo (MTL) 1630",
+        "function": "1000",
+        "height": 12,
+        "year_of_construction": 1986
+      }
+    },
+    {
+      "type": "Feature",
+      "geometry": {
+        "type": "Polygon",
+        "coordinates": [
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+          ]
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+      "id": 175910,
+      "properties": {
+        "name": "01044586",
+        "address": "rue Victor-Hugo (MTL) 1590",
+        "function": "1000",
+        "height": 9,
+        "year_of_construction": 1986
+      }
+    },
+    {
+      "type": "Feature",
+      "geometry": {
+        "type": "Polygon",
+        "coordinates": [
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+        ]
+      },
+      "id": 176056,
+      "properties": {
+        "name": "01044599",
+        "address": "rue Victor-Hugo (MTL) 1620",
+        "function": "1000",
+        "height": 8,
+        "year_of_construction": 1986
+      }
+    },
+    {
+      "type": "Feature",
+      "geometry": {
+        "type": "Polygon",
+        "coordinates": [
+          [
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+              45.49247194194522
+            ]
+          ]
+        ]
+      },
+      "id": 176261,
+      "properties": {
+        "name": "01044613",
+        "address": "rue Victor-Hugo (MTL) 1656",
+        "function": "1000",
+        "height": 10,
+        "year_of_construction": 1986
+      }
+    },
+    {
+      "type": "Feature",
+      "geometry": {
+        "type": "Polygon",
+        "coordinates": [
+          [
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+            ]
+          ]
+        ]
+      },
+      "id": 176293,
+      "properties": {
+        "name": "01044604",
+        "address": "rue Victor-Hugo (MTL) 1636",
+        "function": "1000",
+        "height": 12,
+        "year_of_construction": 1986
+      }
+    },
+    {
+      "type": "Feature",
+      "geometry": {
+        "type": "Polygon",
+        "coordinates": [
+          [
+            [
+              -73.56790222258577,
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+      },
+      "id": 176296,
+      "properties": {
+        "name": "01044611",
+        "address": "rue Victor-Hugo (MTL) 1650",
+        "function": "1000",
+        "height": 10,
+        "year_of_construction": 1986
+      }
+    },
+    {
+      "type": "Feature",
+      "geometry": {
+        "type": "Polygon",
+        "coordinates": [
+          [
+            [
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+        "address": "rue Victor-Hugo (MTL) 1640",
+        "function": "1000",
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+        "year_of_construction": 1986
+      }
+    },
+    {
+      "type": "Feature",
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+      "properties": {
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+        "address": "rue Victor-Hugo (MTL) 1591",
+        "function": "1000",
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+        "year_of_construction": 1987
+      }
+    },
+    {
+      "type": "Feature",
+      "geometry": {
+        "type": "Polygon",
+        "coordinates": [
+          [
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+              -73.56773125185198,
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+          ]
+        ]
+      },
+      "id": 178164,
+      "properties": {
+        "name": "01044615",
+        "address": "rue Victor-Hugo (MTL) 1660",
+        "function": "1000",
+        "height": 9,
+        "year_of_construction": 1986
+      }
+    },
+    {
+      "type": "Feature",
+      "geometry": {
+        "type": "Polygon",
+        "coordinates": [
+          [
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+        "address": "rue Victor-Hugo (MTL) 1596",
+        "function": "1000",
+        "height": 9,
+        "year_of_construction": 1986
+      }
+    },
+    {
+      "type": "Feature",
+      "geometry": {
+        "type": "Polygon",
+        "coordinates": [
+          [
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+              -73.56825635009473,
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+          ]
+        ]
+      },
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+      "properties": {
+        "name": "01044595",
+        "address": "rue Victor-Hugo (MTL) 1610",
+        "function": "1000",
+        "height": 8,
+        "year_of_construction": 1986
+      }
+    },
+    {
+      "type": "Feature",
+      "geometry": {
+        "type": "Polygon",
+        "coordinates": [
+          [
+            [
+              -73.56821589168355,
+              45.491972368627906
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+              45.49201384773851
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+              -73.56821589168355,
+              45.491972368627906
+            ]
+          ]
+        ]
+      },
+      "id": 181310,
+      "properties": {
+        "name": "01044597",
+        "address": "rue Victor-Hugo (MTL) 1616",
+        "function": "1000",
+        "height": 8,
+        "year_of_construction": 1986
+      }
+    },
+    {
+      "type": "Feature",
+      "geometry": {
+        "type": "Polygon",
+        "coordinates": [
+          [
+            [
+              -73.56809506939487,
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+            [
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+            ],
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+            ],
+            [
+              -73.56809506939487,
+              45.49209624228538
+            ]
+          ]
+        ]
+      },
+      "id": 182393,
+      "properties": {
+        "name": "01044601",
+        "address": "rue Victor-Hugo (MTL) 1626",
+        "function": "1000",
+        "height": 8,
+        "year_of_construction": 1986
+      }
+    },
+    {
+      "type": "Feature",
+      "geometry": {
+        "type": "Polygon",
+        "coordinates": [
+          [
+            [
+              -73.56790756893894,
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+            ],
+            [
+              -73.56790222258577,
+              45.49229712328457
+            ],
+            [
+              -73.56803643080562,
+              45.49236123475947
+            ],
+            [
+              -73.56807651582375,
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+            [
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+            ],
+            [
+              -73.56790756893894,
+              45.492291541967774
+            ]
+          ]
+        ]
+      },
+      "id": 182442,
+      "properties": {
+        "name": "01044609",
+        "address": "rue Victor-Hugo (MTL) 1646",
+        "function": "1000",
+        "height": 11,
+        "year_of_construction": 1986
+      }
+    },
+    {
+      "type": "Feature",
+      "geometry": {
+        "type": "Polygon",
+        "coordinates": [
+          [
+            [
+              -73.56829706912258,
+              45.49188914205178
+            ],
+            [
+              -73.56825635009473,
+              45.49193088860213
+            ],
+            [
+              -73.56838787594006,
+              45.49199371809223
+            ],
+            [
+              -73.56842846901456,
+              45.49195154234486
+            ],
+            [
+              -73.56829706912258,
+              45.49188914205178
+            ]
+          ]
+        ]
+      },
+      "id": 182546,
+      "properties": {
+        "name": "01044592",
+        "address": "rue Victor-Hugo (MTL) 1606",
+        "function": "1000",
+        "height": 8,
+        "year_of_construction": 1986
+      }
+    }
+  ]
+}

main.py

@@ -65,4 +65,4 @@ for building in city.buildings:
   costs.loc['global_capital_costs', f'Scenario {SYSTEM_RETROFIT}'].to_csv(
     output_path / f'{building.name}_cc.csv')
   costs.loc['global_maintenance_costs', f'Scenario {SYSTEM_RETROFIT}'].to_csv(
-    output_path / f'{building.name}_m.csv')
+    output_path / f'{building.name}_m.csv')

scripts/costs/total_operational_costs.py

@@ -196,7 +196,7 @@ class TotalOperationalCosts(CostBase):
         if cooling is not None:
           hourly += cooling[i] / 3600
         if dhw is not None:
-          dhw += dhw[i] / 3600
+          hourly += dhw[i] / 3600
         hourly_fuel_consumption.append(hourly)
     else:
       heating = None

scripts/costs/total_operational_incomes.py

@@ -36,11 +36,10 @@ class TotalOperationalIncomes(CostBase):
 
     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 = archetype.income.electricity_export * cte.WATTS_HOUR_TO_JULES * 1000  # to account for unit change
+      price_export = archetype.income.electricity_export  # to account for unit change
       self._yearly_operational_incomes.loc[year, 'Incomes electricity'] = (
-          onsite_electricity_production * price_export * price_increase_electricity
+        (onsite_electricity_production / cte.WATTS_HOUR_TO_JULES) * price_export * price_increase_electricity
       )
 
     self._yearly_operational_incomes.fillna(0, inplace=True)
-    return self._yearly_operational_incomes
+    return self._yearly_operational_incomes

scripts/energy_system_retrofit_report.py

@@ -0,0 +1,595 @@
+import os
+import hub.helpers.constants as cte
+import matplotlib.pyplot as plt
+from matplotlib import cm
+from scripts.report_creation import LatexReport
+from matplotlib.ticker import MaxNLocator
+import numpy as np
+from pathlib import Path
+import glob
+
+
+class EnergySystemRetrofitReport:
+  def __init__(self, city, output_path, retrofit_scenario, current_status_energy_consumption_data,
+               retrofitted_energy_consumption_data, current_status_lcc_data, retrofitted_lcc_data):
+    self.city = city
+    self.current_status_data = current_status_energy_consumption_data
+    self.retrofitted_data = retrofitted_energy_consumption_data
+    self.current_status_lcc = current_status_lcc_data
+    self.retrofitted_lcc = retrofitted_lcc_data
+    self.output_path = output_path
+    self.content = []
+    self.retrofit_scenario = retrofit_scenario
+    self.report = LatexReport('energy_system_retrofit_report',
+                              'Energy System Retrofit Report', self.retrofit_scenario, output_path)
+    self.system_schemas_path = (Path(__file__).parent.parent / 'hub' / 'data' / 'energy_systems' / 'schemas')
+    self.charts_path = Path(output_path) / 'charts'
+    self.charts_path.mkdir(parents=True, exist_ok=True)
+    files = glob.glob(f'{self.charts_path}/*')
+    for file in files:
+      os.remove(file)
+
+  def building_energy_info(self):
+    table_data = [
+      ["Building Name", "Year of Construction", "function", "Yearly Heating Demand (MWh)",
+       "Yearly Cooling Demand (MWh)", "Yearly DHW Demand (MWh)", "Yearly Electricity Demand (MWh)"]
+    ]
+    intensity_table_data = [["Building Name", "Total Floor Area $m^2$", "Heating Demand Intensity kWh/ $m^2$",
+                             "Cooling Demand Intensity kWh/ $m^2$", "Electricity Intensity kWh/ $m^2$"]]
+    peak_load_data = [["Building Name", "Heating Peak Load (kW)", "Cooling Peak Load (kW)",
+                       "Domestic Hot Water Peak Load (kW)"]]
+
+    for building in self.city.buildings:
+      total_floor_area = 0
+      for zone in building.thermal_zones_from_internal_zones:
+        total_floor_area += zone.total_floor_area
+      building_data = [
+        building.name,
+        str(building.year_of_construction),
+        building.function,
+        str(format(building.heating_demand[cte.YEAR][0] / 3.6e9, '.2f')),
+        str(format(building.cooling_demand[cte.YEAR][0] / 3.6e9, '.2f')),
+        str(format(building.domestic_hot_water_heat_demand[cte.YEAR][0] / 3.6e9, '.2f')),
+        str(format(
+          (building.lighting_electrical_demand[cte.YEAR][0] + building.appliances_electrical_demand[cte.YEAR][0])
+          / 3.6e9, '.2f')),
+      ]
+      intensity_data = [
+        building.name,
+        str(format(total_floor_area, '.2f')),
+        str(format(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area), '.2f')),
+        str(format(building.cooling_demand[cte.YEAR][0] / (3.6e6 * total_floor_area), '.2f')),
+        str(format(
+          (building.lighting_electrical_demand[cte.YEAR][0] + building.appliances_electrical_demand[cte.YEAR][0]) /
+          (3.6e6 * total_floor_area), '.2f'))
+      ]
+      peak_data = [
+        building.name,
+        str(format(building.heating_peak_load[cte.YEAR][0] / 1000, '.2f')),
+        str(format(building.cooling_peak_load[cte.YEAR][0] / 1000, '.2f')),
+        str(format(
+          (building.lighting_electrical_demand[cte.YEAR][0] + building.appliances_electrical_demand[cte.YEAR][0]) /
+          (3.6e6 * total_floor_area), '.2f'))
+      ]
+      table_data.append(building_data)
+      intensity_table_data.append(intensity_data)
+      peak_load_data.append(peak_data)
+
+    self.report.add_table(table_data, caption='Buildings Energy Consumption Data')
+    self.report.add_table(intensity_table_data, caption='Buildings Energy Use Intensity Data')
+    self.report.add_table(peak_load_data, caption='Buildings Peak Load Data')
+
+  def plot_monthly_energy_demands(self, data, file_name, title):
+    # Data preparation
+    months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
+    demands = {
+      'Heating': ('heating', '#2196f3'),
+      'Cooling': ('cooling', '#ff5a5f'),
+      'DHW': ('dhw', '#4caf50'),
+      'Electricity': ('lighting_appliance', '#ffc107')
+    }
+
+    # Helper function for plotting
+    def plot_bar_chart(ax, demand_type, color, ylabel, title):
+      values = data[demand_type]
+      ax.bar(months, values, color=color, width=0.6, zorder=2)
+      ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
+      ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)
+      ax.set_xlabel('Month', fontsize=12, labelpad=10)
+      ax.set_ylabel(ylabel, fontsize=14, labelpad=10)
+      ax.set_title(title, fontsize=14, weight='bold', alpha=.8, pad=40)
+      ax.xaxis.set_major_locator(MaxNLocator(integer=True))
+      ax.yaxis.set_major_locator(MaxNLocator(integer=True))
+      ax.set_xticks(np.arange(len(months)))
+      ax.set_xticklabels(months, rotation=45, ha='right')
+      ax.bar_label(ax.containers[0], padding=3, color='black', fontsize=12, rotation=90)
+      ax.spines[['top', 'left', 'bottom']].set_visible(False)
+      ax.spines['right'].set_linewidth(1.1)
+      average_value = np.mean(values)
+      ax.axhline(y=average_value, color='grey', linewidth=2, linestyle='--')
+      ax.text(len(months) - 1, average_value, f'Average = {average_value:.1f} kWh', ha='right', va='bottom',
+              color='grey')
+
+    # Plotting
+    fig, axs = plt.subplots(4, 1, figsize=(20, 16), dpi=96)
+    fig.suptitle(title, fontsize=16, weight='bold', alpha=.8)
+
+    plot_bar_chart(axs[0], 'heating', demands['Heating'][1], 'Heating Demand (kWh)', 'Monthly Heating Demand')
+    plot_bar_chart(axs[1], 'cooling', demands['Cooling'][1], 'Cooling Demand (kWh)', 'Monthly Cooling Demand')
+    plot_bar_chart(axs[2], 'dhw', demands['DHW'][1], 'DHW Demand (kWh)', 'Monthly DHW Demand')
+    plot_bar_chart(axs[3], 'lighting_appliance', demands['Electricity'][1], 'Electricity Demand (kWh)',
+                   'Monthly Electricity Demand')
+
+    # Set a white background
+    fig.patch.set_facecolor('white')
+
+    # Adjust the margins around the plot area
+    plt.subplots_adjust(left=0.05, right=0.95, top=0.9, bottom=0.1, hspace=0.5)
+
+    # Save the plot
+    chart_path = self.charts_path / f'{file_name}.png'
+    plt.savefig(chart_path, bbox_inches='tight')
+    plt.close()
+
+    return chart_path
+
+  def plot_monthly_energy_consumption(self, data, file_name, title):
+    # Data preparation
+    months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
+    consumptions = {
+      'Heating': ('heating', '#2196f3', 'Heating Consumption (kWh)', 'Monthly Energy Consumption for Heating'),
+      'Cooling': ('cooling', '#ff5a5f', 'Cooling Consumption (kWh)', 'Monthly Energy Consumption for Cooling'),
+      'DHW': ('dhw', '#4caf50', 'DHW Consumption (kWh)', 'Monthly DHW Consumption')
+    }
+
+    # Helper function for plotting
+    def plot_bar_chart(ax, consumption_type, color, ylabel, title):
+      values = data[consumption_type]
+      ax.bar(months, values, color=color, width=0.6, zorder=2)
+      ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
+      ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)
+      ax.set_xlabel('Month', fontsize=12, labelpad=10)
+      ax.set_ylabel(ylabel, fontsize=14, labelpad=10)
+      ax.set_title(title, fontsize=14, weight='bold', alpha=.8, pad=40)
+      ax.xaxis.set_major_locator(MaxNLocator(integer=True))
+      ax.yaxis.set_major_locator(MaxNLocator(integer=True))
+      ax.set_xticks(np.arange(len(months)))
+      ax.set_xticklabels(months, rotation=45, ha='right')
+      ax.bar_label(ax.containers[0], padding=3, color='black', fontsize=12, rotation=90)
+      ax.spines[['top', 'left', 'bottom']].set_visible(False)
+      ax.spines['right'].set_linewidth(1.1)
+      average_value = np.mean(values)
+      ax.axhline(y=average_value, color='grey', linewidth=2, linestyle='--')
+      ax.text(len(months) - 1, average_value, f'Average = {average_value:.1f} kWh', ha='right', va='bottom',
+              color='grey')
+
+    # Plotting
+    fig, axs = plt.subplots(3, 1, figsize=(20, 15), dpi=96)
+    fig.suptitle(title, fontsize=16, weight='bold', alpha=.8)
+
+    plot_bar_chart(axs[0], 'heating', consumptions['Heating'][1], consumptions['Heating'][2],
+                   consumptions['Heating'][3])
+    plot_bar_chart(axs[1], 'cooling', consumptions['Cooling'][1], consumptions['Cooling'][2],
+                   consumptions['Cooling'][3])
+    plot_bar_chart(axs[2], 'dhw', consumptions['DHW'][1], consumptions['DHW'][2], consumptions['DHW'][3])
+
+    # Set a white background
+    fig.patch.set_facecolor('white')
+
+    # Adjust the margins around the plot area
+    plt.subplots_adjust(left=0.05, right=0.95, top=0.9, bottom=0.1, wspace=0.3, hspace=0.5)
+
+    # Save the plot
+    chart_path = self.charts_path / f'{file_name}.png'
+    plt.savefig(chart_path, bbox_inches='tight')
+    plt.close()
+
+    return chart_path
+
+  def fuel_consumption_breakdown(self, file_name, data):
+    fuel_consumption_breakdown = {}
+    for building in self.city.buildings:
+      for key, breakdown in data[f'{building.name}']['energy_consumption_breakdown'].items():
+        if key not in fuel_consumption_breakdown:
+          fuel_consumption_breakdown[key] = {sector: 0 for sector in breakdown}
+        for sector, value in breakdown.items():
+          if sector in fuel_consumption_breakdown[key]:
+            fuel_consumption_breakdown[key][sector] += value / 3.6e6
+          else:
+            fuel_consumption_breakdown[key][sector] = value / 3.6e6
+
+    plt.style.use('ggplot')
+    num_keys = len(fuel_consumption_breakdown)
+    fig, axs = plt.subplots(1 if num_keys <= 2 else num_keys, min(num_keys, 2), figsize=(12, 5))
+    axs = axs if num_keys > 1 else [axs]  # Ensure axs is always iterable
+
+    for i, (fuel, breakdown) in enumerate(fuel_consumption_breakdown.items()):
+      labels = breakdown.keys()
+      values = breakdown.values()
+      colors = cm.get_cmap('tab20c', len(labels))
+      ax = axs[i] if num_keys > 1 else axs[0]
+      ax.pie(values, labels=labels,
+             autopct=lambda pct: f"{pct:.1f}%\n({pct / 100 * sum(values):.2f})",
+             startangle=90, colors=[colors(j) for j in range(len(labels))])
+      ax.set_title(f'{fuel} Consumption Breakdown')
+
+    plt.suptitle('City Energy Consumption Breakdown', fontsize=16, fontweight='bold')
+    plt.tight_layout(rect=[0, 0, 1, 0.95])  # Adjust layout to fit the suptitle
+
+    chart_path = self.charts_path / f'{file_name}.png'
+    plt.savefig(chart_path, dpi=300)
+    plt.close()
+    return chart_path
+
+  def energy_system_archetype_schematic(self):
+    energy_system_archetypes = {}
+    for building in self.city.buildings:
+      if building.energy_systems_archetype_name not in energy_system_archetypes:
+        energy_system_archetypes[building.energy_systems_archetype_name] = [building.name]
+      else:
+        energy_system_archetypes[building.energy_systems_archetype_name].append(building.name)
+
+    text = ""
+    items = ""
+    for archetype, buildings in energy_system_archetypes.items():
+      buildings_str = ", ".join(buildings)
+      text += f"Figure 4 shows the schematic of the proposed energy system for buildings {buildings_str}.\n"
+      if archetype in ['PV+4Pipe+DHW', 'PV+ASHP+GasBoiler+TES']:
+        text += "This energy system archetype is formed of the following systems: \par"
+        items = ['Rooftop Photovoltaic System: The rooftop PV system is tied to the grid and in case there is surplus '
+                 'energy, it is sold to Hydro-Quebec through their Net-Meterin program.',
+                 '4-Pipe HVAC System: This systems includes a 4-pipe heat pump capable of generating heat and cooling '
+                 'at the same time, a natural gas boiler as the auxiliary heating system, and a hot water storage tank.'
+                 'The temperature inside the tank is kept between 40-55 C. The cooling demand is totally supplied by '
+                 'the heat pump unit.',
+                 'Domestic Hot Water Heat Pump System: This system is in charge of supplying domestic hot water demand.'
+                 'The heat pump is connected to a thermal storage tank with electric resistance heating coil inside it.'
+                 ' The temperature inside the tank should always remain above 60 C.']
+
+      self.report.add_text(text)
+      self.report.add_itemize(items=items)
+      schema_path = self.system_schemas_path / f'{archetype}.jpg'
+      self.report.add_image(str(schema_path).replace('\\', '/'),
+                            f'Proposed energy system for buildings {buildings_str}')
+
+  def plot_monthly_radiation(self):
+    months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
+    monthly_roof_radiation = []
+    for i in range(len(months)):
+      tilted_radiation = 0
+      for building in self.city.buildings:
+        tilted_radiation += (building.roofs[0].global_irradiance_tilted[cte.MONTH][i] /
+                             (cte.WATTS_HOUR_TO_JULES * 1000))
+      monthly_roof_radiation.append(tilted_radiation)
+
+    def plot_bar_chart(ax, months, values, color, ylabel, title):
+      ax.bar(months, values, color=color, width=0.6, zorder=2)
+      ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
+      ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)
+      ax.set_xlabel('Month', fontsize=12, labelpad=10)
+      ax.set_ylabel(ylabel, fontsize=14, labelpad=10)
+      ax.set_title(title, fontsize=14, weight='bold', alpha=.8, pad=40)
+      ax.xaxis.set_major_locator(MaxNLocator(integer=True))
+      ax.yaxis.set_major_locator(MaxNLocator(integer=True))
+      ax.set_xticks(np.arange(len(months)))
+      ax.set_xticklabels(months, rotation=45, ha='right')
+      ax.bar_label(ax.containers[0], padding=3, color='black', fontsize=12, rotation=90)
+      ax.spines[['top', 'left', 'bottom']].set_visible(False)
+      ax.spines['right'].set_linewidth(1.1)
+      average_value = np.mean(values)
+      ax.axhline(y=average_value, color='grey', linewidth=2, linestyle='--')
+      ax.text(len(months) - 1, average_value, f'Average = {average_value:.1f} kWh', ha='right', va='bottom',
+              color='grey')
+
+    # Plotting the bar chart
+    fig, ax = plt.subplots(figsize=(15, 8), dpi=96)
+    plot_bar_chart(ax, months, monthly_roof_radiation, '#ffc107', 'Tilted Roof Radiation (kWh / m2)',
+                   'Monthly Tilted Roof Radiation')
+
+    # Set a white background
+    fig.patch.set_facecolor('white')
+
+    # Adjust the margins around the plot area
+    plt.subplots_adjust(left=0.1, right=0.95, top=0.9, bottom=0.1)
+
+    # Save the plot
+    chart_path = self.charts_path / 'monthly_tilted_roof_radiation.png'
+    plt.savefig(chart_path, bbox_inches='tight')
+    plt.close()
+    return chart_path
+
+  def energy_consumption_comparison(self, current_status_data, retrofitted_data, file_name, title):
+    months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
+    consumptions = {
+      'Heating': ('heating', '#2196f3', 'Heating Consumption (kWh)', 'Monthly Energy Consumption for Heating'),
+      'Cooling': ('cooling', '#ff5a5f', 'Cooling Consumption (kWh)', 'Monthly Energy Consumption for Cooling'),
+      'DHW': ('dhw', '#4caf50', 'DHW Consumption (kWh)', 'Monthly DHW Consumption')
+    }
+
+    # Helper function for plotting
+    def plot_double_bar_chart(ax, consumption_type, color, ylabel, title):
+      current_values = current_status_data[consumption_type]
+      retrofitted_values = retrofitted_data[consumption_type]
+      bar_width = 0.35
+      index = np.arange(len(months))
+
+      ax.bar(index, current_values, bar_width, label='Current Status', color=color, alpha=0.7, zorder=2)
+      ax.bar(index + bar_width, retrofitted_values, bar_width, label='Retrofitted', color=color, hatch='/', zorder=2)
+
+      ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
+      ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)
+      ax.set_xlabel('Month', fontsize=12, labelpad=10)
+      ax.set_ylabel(ylabel, fontsize=14, labelpad=10)
+      ax.set_title(title, fontsize=14, weight='bold', alpha=.8, pad=40)
+      ax.set_xticks(index + bar_width / 2)
+      ax.set_xticklabels(months, rotation=45, ha='right')
+      ax.legend()
+
+      # Adding bar labels
+      ax.bar_label(ax.containers[0], padding=3, color='black', fontsize=12, rotation=90)
+      ax.bar_label(ax.containers[1], padding=3, color='black', fontsize=12, rotation=90)
+
+      ax.spines[['top', 'left', 'bottom']].set_visible(False)
+      ax.spines['right'].set_linewidth(1.1)
+
+    # Plotting
+    fig, axs = plt.subplots(3, 1, figsize=(20, 25), dpi=96)
+    fig.suptitle(title, fontsize=16, weight='bold', alpha=.8)
+
+    plot_double_bar_chart(axs[0], 'heating', consumptions['Heating'][1], consumptions['Heating'][2],
+                          consumptions['Heating'][3])
+    plot_double_bar_chart(axs[1], 'cooling', consumptions['Cooling'][1], consumptions['Cooling'][2],
+                          consumptions['Cooling'][3])
+    plot_double_bar_chart(axs[2], 'dhw', consumptions['DHW'][1], consumptions['DHW'][2], consumptions['DHW'][3])
+
+    # Set a white background
+    fig.patch.set_facecolor('white')
+
+    # Adjust the margins around the plot area
+    plt.subplots_adjust(left=0.05, right=0.95, top=0.9, bottom=0.1, wspace=0.3, hspace=0.5)
+
+    # Save the plot
+    chart_path = self.charts_path / f'{file_name}.png'
+    plt.savefig(chart_path, bbox_inches='tight')
+    plt.close()
+
+    return chart_path
+
+  def yearly_consumption_comparison(self):
+    current_total_consumption = round(self.current_status_data['total_consumption'], 2)
+    retrofitted_total_consumption = round(self.retrofitted_data['total_consumption'], 2)
+    text = (
+      f'The total yearly energy consumption before and after the retrofit are {current_total_consumption} MWh and '
+      f'{retrofitted_total_consumption} MWh, respectively.')
+    if retrofitted_total_consumption < current_total_consumption:
+      change = str(round((current_total_consumption - retrofitted_total_consumption) * 100 / current_total_consumption,
+                         2))
+      text += f'Therefore, the total yearly energy consumption decreased by {change} \%.'
+    else:
+      change = str(round((retrofitted_total_consumption - current_total_consumption) * 100 /
+                         retrofitted_total_consumption, 2))
+      text += f'Therefore, the total yearly energy consumption increased by {change} \%. \par'
+    self.report.add_text(text)
+
+  def pv_system(self):
+    self.report.add_text('The first step in PV assessments is evaluating the potential of buildings for installing '
+                         'rooftop PV system. The benchmark value used for this evaluation is the mean yearly solar '
+                         'incident in Montreal. According to Hydro-Quebec, the mean annual incident in Montreal is 1350'
+                         'kWh/m2. Therefore, any building with rooftop annual global horizontal radiation of less than '
+                         '1080 kWh/m2 is considered to be infeasible. Table 4 shows the yearly horizontal radiation on '
+                         'buildings roofs. \par')
+    radiation_data = [
+      ["Building Name", "Roof Area $m^2$", "Function", "Rooftop Annual Global Horizontal Radiation kWh/ $m^2$"]
+    ]
+    pv_feasible_buildings = []
+    for building in self.city.buildings:
+      if building.roofs[0].global_irradiance[cte.YEAR][0] > 1080:
+        pv_feasible_buildings.append(building.name)
+      data = [building.name, str(format(building.roofs[0].perimeter_area, '.2f')), building.function,
+              str(format(building.roofs[0].global_irradiance[cte.YEAR][0] / (cte.WATTS_HOUR_TO_JULES * 1000), '.2f'))]
+      radiation_data.append(data)
+
+    self.report.add_table(radiation_data,
+                          caption='Buildings Roof Characteristics')
+
+    if len(pv_feasible_buildings) == len(self.city.buildings):
+      buildings_str = 'all'
+    else:
+      buildings_str = ", ".join(pv_feasible_buildings)
+    self.report.add_text(f"From the table it can be seen that {buildings_str} buildings are good candidates to have "
+                         f"rooftop PV system. The next step is calculating the amount of solar radiation on a tilted "
+                         f"surface. Figure 5 shows the total monthly solar radiation on a surface with the tilt angle "
+                         f"of 45 degrees on the roofs of those buildings that are identified to have rooftop PV system."
+                         f"\par")
+    tilted_radiation = self.plot_monthly_radiation()
+    self.report.add_image(str(tilted_radiation).replace('\\', '/'),
+                          caption='Total Monthly Solar Radiation on Buildings Roofs on a 45 Degrees Tilted Surface',
+                          placement='H')
+    self.report.add_text('The first step in sizing the PV system is to find the available roof area. '
+                         'Few considerations need to be made here. The considerations include space for maintenance '
+                         'crew, space for mechanical equipment, and orientation correction factor to make sure all '
+                         'the panel are truly facing south. After all these considerations, the minimum distance '
+                         'between the panels to avoid shading throughout the year is found. Table 5 shows the number of'
+                         'panles on each buildings roof, yearly PV production, total electricity consumption, and self '
+                         'consumption. \par')
+
+    pv_output_table = [['Building Name', 'Total Surface Area of PV Panels ($m^2$)',
+                        'Total Solar Incident on PV Modules (MWh)', 'Yearly PV production (MWh)']]
+
+    for building in self.city.buildings:
+      if building.name in pv_feasible_buildings:
+        pv_data = []
+        pv_data.append(building.name)
+        yearly_solar_incident = (building.roofs[0].global_irradiance_tilted[cte.YEAR][0] *
+                                 building.roofs[0].installed_solar_collector_area) / (cte.WATTS_HOUR_TO_JULES * 1e6)
+        yearly_solar_incident_str = format(yearly_solar_incident, '.2f')
+        yearly_pv_output = building.onsite_electrical_production[cte.YEAR][0] / (cte.WATTS_HOUR_TO_JULES * 1e6)
+        yearly_pv_output_str = format(yearly_pv_output, '.2f')
+
+        pv_data.append(format(building.roofs[0].installed_solar_collector_area, '.2f'))
+        pv_data.append(yearly_solar_incident_str)
+        pv_data.append(yearly_pv_output_str)
+
+        pv_output_table.append(pv_data)
+
+    self.report.add_table(pv_output_table, caption='PV System Simulation Results', first_column_width=3)
+
+  def life_cycle_cost_stacked_bar(self, file_name, title):
+    # Aggregate LCC components for current and retrofitted statuses
+    current_status_capex = 0
+    current_status_opex = 0
+    current_status_maintenance = 0
+    current_status_end_of_life = 0
+    retrofitted_capex = 0
+    retrofitted_opex = 0
+    retrofitted_maintenance = 0
+    retrofitted_end_of_life = 0
+
+    for building in self.city.buildings:
+      current_status_capex += self.current_status_lcc[f'{building.name}']['capital_cost_per_sqm']
+      retrofitted_capex += self.retrofitted_lcc[f'{building.name}']['capital_cost_per_sqm']
+      current_status_opex += self.current_status_lcc[f'{building.name}']['operational_cost_per_sqm']
+      retrofitted_opex += self.retrofitted_lcc[f'{building.name}']['operational_cost_per_sqm']
+      current_status_maintenance += self.current_status_lcc[f'{building.name}']['maintenance_cost_per_sqm']
+      retrofitted_maintenance += self.retrofitted_lcc[f'{building.name}']['maintenance_cost_per_sqm']
+      current_status_end_of_life += self.current_status_lcc[f'{building.name}']['end_of_life_cost_per_sqm']
+      retrofitted_end_of_life += self.retrofitted_lcc[f'{building.name}']['end_of_life_cost_per_sqm']
+
+    current_status_lcc_components_sqm = {
+      'Capital Cost': current_status_capex / len(self.city.buildings),
+      'Operational Cost': current_status_opex / len(self.city.buildings),
+      'Maintenance Cost': current_status_maintenance / len(self.city.buildings),
+      'End of Life Cost': current_status_end_of_life / len(self.city.buildings)
+    }
+    retrofitted_lcc_components_sqm = {
+      'Capital Cost': retrofitted_capex / len(self.city.buildings),
+      'Operational Cost': retrofitted_opex / len(self.city.buildings),
+      'Maintenance Cost': retrofitted_maintenance / len(self.city.buildings),
+      'End of Life Cost': retrofitted_end_of_life / len(self.city.buildings)
+    }
+
+    labels = ['Current Status', 'Retrofitted Status']
+    categories = ['Capital Cost', 'Operational Cost', 'Maintenance Cost', 'End of Life Cost']
+    current_values = list(current_status_lcc_components_sqm.values())
+    retrofitted_values = list(retrofitted_lcc_components_sqm.values())
+    colors = ['#2196f3', '#ff5a5f', '#4caf50', '#ffc107']
+
+    # Data preparation
+    bar_width = 0.35
+    r = np.arange(len(labels))
+
+    fig, ax = plt.subplots(figsize=(12, 8), dpi=96)
+    fig.suptitle(title, fontsize=16, weight='bold', alpha=.8)
+
+    # Plotting current status data
+    bottom = np.zeros(2)
+    for i, (category, color) in enumerate(zip(categories, colors)):
+      values = [current_status_lcc_components_sqm[category], retrofitted_lcc_components_sqm[category]]
+      ax.bar(r, values, bottom=bottom, color=color, edgecolor='white', width=bar_width, label=category)
+      bottom += values
+
+    # Adding summation annotations at the top of the bars
+    for idx, (x, total) in enumerate(zip(r, bottom)):
+      ax.text(x, total, f'{total:.1f}', ha='center', va='bottom', fontsize=12, fontweight='bold')
+
+    # Adding labels, title, and grid
+    ax.set_xlabel('LCC Components', fontsize=12, labelpad=10)
+    ax.set_ylabel('Average Cost (CAD/m²)', fontsize=14, labelpad=10)
+    ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)
+    ax.set_xticks(r)
+    ax.set_xticklabels(labels, rotation=45, ha='right')
+    ax.legend()
+
+    # Adding a white background
+    fig.patch.set_facecolor('white')
+
+    # Adjusting the margins around the plot area
+    plt.subplots_adjust(left=0.05, right=0.95, top=0.9, bottom=0.2)
+
+    # Save the plot
+    chart_path = self.charts_path / f'{file_name}.png'
+    plt.savefig(chart_path, bbox_inches='tight')
+    plt.close()
+
+    return chart_path
+
+  def create_report(self):
+    # Add sections and text to the report
+    self.report.add_section('Overview of the Current Status in Buildings')
+    self.report.add_text('In this section, an overview of the current status of buildings characteristics, '
+                         'energy demand and consumptions are provided')
+    self.report.add_subsection('Buildings Characteristics')
+
+    self.building_energy_info()
+
+    # current monthly demands and consumptions
+    current_monthly_demands = self.current_status_data['monthly_demands']
+    current_monthly_consumptions = self.current_status_data['monthly_consumptions']
+
+    # Plot and save demand chart
+    current_demand_chart_path = self.plot_monthly_energy_demands(data=current_monthly_demands,
+                                                                 file_name='current_monthly_demands',
+                                                                 title='Current Status Monthly Energy Demands')
+    # Plot and save consumption chart
+    current_consumption_chart_path = self.plot_monthly_energy_consumption(data=current_monthly_consumptions,
+                                                                          file_name='monthly_consumptions',
+                                                                          title='Monthly Energy Consumptions')
+    current_consumption_breakdown_path = self.fuel_consumption_breakdown('City_Energy_Consumption_Breakdown',
+                                                                         self.current_status_data)
+    retrofitted_consumption_breakdown_path = self.fuel_consumption_breakdown(
+      'fuel_consumption_breakdown_after_retrofit',
+      self.retrofitted_data)
+    life_cycle_cost_sqm_stacked_bar_chart_path = self.life_cycle_cost_stacked_bar('lcc_per_sqm',
+                                                                                  'LCC Analysis')
+    # Add current state of energy demands in the city
+    self.report.add_subsection('Current State of Energy Demands in the City')
+    self.report.add_text('The total monthly energy demands in the city are shown in Figure 1. It should be noted '
+                         'that the electricity demand refers to total lighting and appliance electricity demands')
+    self.report.add_image(str(current_demand_chart_path).replace('\\', '/'),
+                          'Total Monthly Energy Demands in City',
+                          placement='h')
+
+    # Add current state of energy consumption in the city
+    self.report.add_subsection('Current State of Energy Consumption in the City')
+    self.report.add_text('The following figure shows the amount of energy consumed to supply heating, cooling, and '
+                         'domestic hot water needs in the city. The details of the systems in each building before '
+                         'and after retrofit are provided in Section 4. \par')
+    self.report.add_image(str(current_consumption_chart_path).replace('\\', '/'),
+                          'Total Monthly Energy Consumptions in City',
+                          placement='H')
+    self.report.add_text('Figure 3 shows the yearly energy supplied to the city by fuel in different sectors. '
+                         'All the values are in kWh.')
+    self.report.add_image(str(current_consumption_breakdown_path).replace('\\', '/'),
+                          'Current Energy Consumption Breakdown in the City by Fuel',
+                          placement='H')
+    self.report.add_section(f'{self.retrofit_scenario}')
+    self.report.add_subsection('Proposed Systems')
+    self.energy_system_archetype_schematic()
+    if 'PV' in self.retrofit_scenario:
+      self.report.add_subsection('Rooftop Photovoltaic System Implementation')
+      self.pv_system()
+    if 'System' in self.retrofit_scenario:
+      self.report.add_subsection('Retrofitted HVAC and DHW Systems')
+      self.report.add_text('Figure 6 shows a comparison between total monthly energy consumption in the selected '
+                           'buildings before and after retrofitting.')
+      consumption_comparison = self.energy_consumption_comparison(self.current_status_data['monthly_consumptions'],
+                                                                  self.retrofitted_data['monthly_consumptions'],
+                                                                  'energy_consumption_comparison_in_city',
+                                                                  'Total Monthly Energy Consumption Comparison in '
+                                                                  'Buildings')
+      self.report.add_image(str(consumption_comparison).replace('\\', '/'),
+                            caption='Comparison of Total Monthly Energy Consumption in City Buildings',
+                            placement='H')
+      self.yearly_consumption_comparison()
+      self.report.add_text('Figure 7 shows the fuel consumption breakdown in the area after the retrofit.')
+      self.report.add_image(str(retrofitted_consumption_breakdown_path).replace('\\', '/'),
+                            caption=f'Fuel Consumption Breakdown After {self.retrofit_scenario}',
+                            placement='H')
+    self.report.add_subsection('Life Cycle Cost Analysis')
+    self.report.add_image(str(life_cycle_cost_sqm_stacked_bar_chart_path).replace('\\', '/'),
+                          caption='Average Life Cycle Cost Components',
+                          placement='H')
+
+    # Save and compile the report
+    self.report.save_report()
+    self.report.compile_to_pdf()

scripts/energy_system_retrofit_results.py

@@ -1,68 +1,176 @@
 import hub.helpers.constants as cte
 
 
-def system_results(buildings):
-  system_performance_summary = {}
-  fields = ["Energy System Archetype", "Heating Equipments", "Cooling Equipments", "DHW Equipments",
-            "Photovoltaic System Capacity", "Heating Fuel", "Yearly HVAC Energy Consumption (MWh)",
-            "DHW Energy Consumption (MWH)", "PV Yearly Production (kWh)", "LCC Analysis Duration (Years)",
-            "Energy System Capital Cost (CAD)", "Energy System Average Yearly Operational Cost (CAD)",
-            "Energy System Life Cycle Cost (CAD)"]
-  for building in buildings:
-    system_performance_summary[f'{building.name}'] = {}
-    for field in fields:
-      system_performance_summary[f'{building.name}'][field] = '-'
-
-  for building in buildings:
-    fuels = []
-    system_performance_summary[f'{building.name}']['Energy System Archetype'] = building.energy_systems_archetype_name
-    energy_systems = building.energy_systems
+def hourly_electricity_consumption_profile(building):
+  hourly_electricity_consumption = []
+  energy_systems = building.energy_systems
+  appliance = building.appliances_electrical_demand[cte.HOUR]
+  lighting = building.lighting_electrical_demand[cte.HOUR]
+  elec_heating = 0
+  elec_cooling = 0
+  elec_dhw = 0
+  if cte.HEATING in building.energy_consumption_breakdown[cte.ELECTRICITY]:
+    elec_heating = 1
+  if cte.COOLING in building.energy_consumption_breakdown[cte.ELECTRICITY]:
+    elec_cooling = 1
+  if cte.DOMESTIC_HOT_WATER in building.energy_consumption_breakdown[cte.ELECTRICITY]:
+    elec_dhw = 1
+  heating = None
+  cooling = None
+  dhw = None
+  if elec_heating == 1:
     for energy_system in energy_systems:
-      demand_types = energy_system.demand_types
-      for demand_type in demand_types:
-        if demand_type == cte.COOLING:
-          equipments = []
-          for generation_system in energy_system.generation_systems:
-            if generation_system.fuel_type == cte.ELECTRICITY:
-              equipments.append(generation_system.name or generation_system.system_type)
-            cooling_equipments = ", ".join(equipments)
-            system_performance_summary[f'{building.name}']['Cooling Equipments'] = cooling_equipments
-        elif demand_type == cte.HEATING:
-          equipments = []
-          for generation_system in energy_system.generation_systems:
-            if generation_system.nominal_heat_output is not None:
-              equipments.append(generation_system.name or generation_system.system_type)
-              fuels.append(generation_system.fuel_type)
-            heating_equipments = ", ".join(equipments)
-            system_performance_summary[f'{building.name}']['Heating Equipments'] = heating_equipments
-        elif demand_type == cte.DOMESTIC_HOT_WATER:
-          equipments = []
-          for generation_system in energy_system.generation_systems:
-            equipments.append(generation_system.name or generation_system.system_type)
-            dhw_equipments = ", ".join(equipments)
-            system_performance_summary[f'{building.name}']['DHW Equipments'] = dhw_equipments
-      for generation_system in energy_system.generation_systems:
-        if generation_system.system_type == cte.PHOTOVOLTAIC:
-          system_performance_summary[f'{building.name}'][
-            'Photovoltaic System Capacity'] = generation_system.nominal_electricity_output or str(0)
-    heating_fuels = ", ".join(fuels)
-    system_performance_summary[f'{building.name}']['Heating Fuel'] = heating_fuels
-    system_performance_summary[f'{building.name}']['Yearly HVAC Energy Consumption (MWh)'] = format(
-      (building.heating_consumption[cte.YEAR][0] + building.cooling_consumption[cte.YEAR][0]) / 3.6e9, '.2f')
-    system_performance_summary[f'{building.name}']['DHW Energy Consumption (MWH)'] = format(
-      building.domestic_hot_water_consumption[cte.YEAR][0] / 1e6, '.2f')
-  return system_performance_summary
+      if cte.HEATING in energy_system.demand_types:
+        for generation_system in energy_system.generation_systems:
+          if generation_system.fuel_type == cte.ELECTRICITY:
+            if cte.HEATING in generation_system.energy_consumption:
+              heating = generation_system.energy_consumption[cte.HEATING][cte.HOUR]
+            else:
+              if len(energy_system.generation_systems) > 1:
+                heating = [x / 2 for x in building.heating_consumption[cte.HOUR]]
+              else:
+                heating = building.heating_consumption[cte.HOUR]
+  if elec_dhw == 1:
+    for energy_system in energy_systems:
+      if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
+        for generation_system in energy_system.generation_systems:
+          if generation_system.fuel_type == cte.ELECTRICITY:
+            if cte.DOMESTIC_HOT_WATER in generation_system.energy_consumption:
+              dhw = generation_system.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR]
+            else:
+              if len(energy_system.generation_systems) > 1:
+                dhw = [x / 2 for x in building.domestic_hot_water_consumption[cte.HOUR]]
+              else:
+                dhw = building.domestic_hot_water_consumption[cte.HOUR]
+
+  if elec_cooling == 1:
+    for energy_system in energy_systems:
+      if cte.COOLING in energy_system.demand_types:
+        for generation_system in energy_system.generation_systems:
+          if cte.COOLING in generation_system.energy_consumption:
+            cooling = generation_system.energy_consumption[cte.COOLING][cte.HOUR]
+          else:
+            if len(energy_system.generation_systems) > 1:
+              cooling = [x / 2 for x in building.cooling_consumption[cte.HOUR]]
+            else:
+              cooling = building.cooling_consumption[cte.HOUR]
+
+  for i in range(len(building.heating_demand[cte.HOUR])):
+    hourly = 0
+    hourly += appliance[i] / 3600
+    hourly += lighting[i] / 3600
+    if heating is not None:
+      hourly += heating[i] / 3600
+    if cooling is not None:
+      hourly += cooling[i] / 3600
+    if dhw is not None:
+      hourly += dhw[i] / 3600
+    hourly_electricity_consumption.append(hourly)
+  return hourly_electricity_consumption
 
 
-def new_system_results(buildings):
-  new_system_performance_summary = {}
-  fields = ["Energy System Archetype", "Heating Equipments", "Cooling Equipments", "DHW Equipments",
-            "Photovoltaic System Capacity", "Heating Fuel", "Yearly HVAC Energy Consumption (MWh)",
-            "DHW Energy Consumption (MWH)", "PV Yearly Production (kWh)", "LCC Analysis Duration (Years)",
-            "Energy System Capital Cost (CAD)", "Energy System Average Yearly Operational Cost (CAD)",
-            "Energy System Life Cycle Cost (CAD)"]
-  for building in buildings:
-    new_system_performance_summary[f'{building.name}'] = {}
-    for field in fields:
-      new_system_performance_summary[f'{building.name}'][field] = '-'
-  return new_system_performance_summary
+def consumption_data(city):
+  energy_consumption_data = {}
+  for building in city.buildings:
+    hourly_electricity_consumption = hourly_electricity_consumption_profile(building)
+    energy_consumption_data[f'{building.name}'] = {'heating_consumption': building.heating_consumption,
+                                                   'cooling_consumption': building.cooling_consumption,
+                                                   'domestic_hot_water_consumption':
+                                                   building.domestic_hot_water_consumption,
+                                                   'energy_consumption_breakdown':
+                                                   building.energy_consumption_breakdown,
+                                                   'hourly_electricity_consumption': hourly_electricity_consumption}
+  peak_electricity_consumption = 0
+  for building in energy_consumption_data:
+    peak_electricity_consumption += max(energy_consumption_data[building]['hourly_electricity_consumption'])
+  heating_demand_monthly = []
+  cooling_demand_monthly = []
+  dhw_demand_monthly = []
+  lighting_appliance_monthly = []
+  heating_consumption_monthly = []
+  cooling_consumption_monthly = []
+  dhw_consumption_monthly = []
+  for i in range(12):
+    heating_demand = 0
+    cooling_demand = 0
+    dhw_demand = 0
+    lighting_appliance_demand = 0
+    heating_consumption = 0
+    cooling_consumption = 0
+    dhw_consumption = 0
+    for building in city.buildings:
+      heating_demand += building.heating_demand[cte.MONTH][i] / 3.6e6
+      cooling_demand += building.cooling_demand[cte.MONTH][i] / 3.6e6
+      dhw_demand += building.domestic_hot_water_heat_demand[cte.MONTH][i] / 3.6e6
+      lighting_appliance_demand += building.lighting_electrical_demand[cte.MONTH][i] / 3.6e6
+      heating_consumption += building.heating_consumption[cte.MONTH][i] / 3.6e6
+      if building.cooling_demand[cte.YEAR][0] == 0:
+        cooling_consumption += building.cooling_demand[cte.MONTH][i] / (3.6e6 * 2)
+      else:
+        cooling_consumption += building.cooling_consumption[cte.MONTH][i] / 3.6e6
+      dhw_consumption += building.domestic_hot_water_consumption[cte.MONTH][i] / 3.6e6
+    heating_demand_monthly.append(heating_demand)
+    cooling_demand_monthly.append(cooling_demand)
+    dhw_demand_monthly.append(dhw_demand)
+    lighting_appliance_monthly.append(lighting_appliance_demand)
+    heating_consumption_monthly.append(heating_consumption)
+    cooling_consumption_monthly.append(cooling_consumption)
+    dhw_consumption_monthly.append(dhw_consumption)
+
+  monthly_demands = {'heating': heating_demand_monthly,
+                     'cooling': cooling_demand_monthly,
+                     'dhw': dhw_demand_monthly,
+                     'lighting_appliance': lighting_appliance_monthly}
+  monthly_consumptions = {'heating': heating_consumption_monthly,
+                          'cooling': cooling_consumption_monthly,
+                          'dhw': dhw_consumption_monthly}
+  yearly_heating = 0
+  yearly_cooling = 0
+  yearly_dhw = 0
+  yearly_appliance = 0
+  yearly_lighting = 0
+  for building in city.buildings:
+    yearly_appliance += building.appliances_electrical_demand[cte.YEAR][0] / 3.6e9
+    yearly_lighting += building.lighting_electrical_demand[cte.YEAR][0] / 3.6e9
+    yearly_heating += building.heating_consumption[cte.YEAR][0] / 3.6e9
+    yearly_cooling += building.cooling_consumption[cte.YEAR][0] / 3.6e9
+    yearly_dhw += building.domestic_hot_water_consumption[cte.YEAR][0] / 3.6e9
+
+  total_consumption = yearly_heating + yearly_cooling + yearly_dhw + yearly_appliance + yearly_lighting
+  energy_consumption_data['monthly_demands'] = monthly_demands
+  energy_consumption_data['monthly_consumptions'] = monthly_consumptions
+  energy_consumption_data['total_consumption'] = total_consumption
+  energy_consumption_data['maximum_hourly_electricity_consumption'] = peak_electricity_consumption
+
+  return energy_consumption_data
+
+
+def cost_data(building, lcc_dataframe, cost_retrofit_scenario):
+  total_floor_area = 0
+  for thermal_zone in building.thermal_zones_from_internal_zones:
+    total_floor_area += thermal_zone.total_floor_area
+  capital_cost = lcc_dataframe.loc['total_capital_costs_systems', f'Scenario {cost_retrofit_scenario}']
+  operational_cost = lcc_dataframe.loc['total_operational_costs', f'Scenario {cost_retrofit_scenario}']
+  maintenance_cost = lcc_dataframe.loc['total_maintenance_costs', f'Scenario {cost_retrofit_scenario}']
+  end_of_life_cost = lcc_dataframe.loc['end_of_life_costs', f'Scenario {cost_retrofit_scenario}']
+  operational_income = lcc_dataframe.loc['operational_incomes', f'Scenario {cost_retrofit_scenario}']
+  total_life_cycle_cost = capital_cost + operational_cost + maintenance_cost + end_of_life_cost + operational_income
+  specific_capital_cost = capital_cost / total_floor_area
+  specific_operational_cost = operational_cost / total_floor_area
+  specific_maintenance_cost = maintenance_cost / total_floor_area
+  specific_end_of_life_cost = end_of_life_cost / total_floor_area
+  specific_operational_income = operational_income / total_floor_area
+  specific_life_cycle_cost = total_life_cycle_cost / total_floor_area
+  life_cycle_cost_analysis = {'capital_cost': capital_cost,
+                              'capital_cost_per_sqm': specific_capital_cost,
+                              'operational_cost': operational_cost,
+                              'operational_cost_per_sqm': specific_operational_cost,
+                              'maintenance_cost': maintenance_cost,
+                              'maintenance_cost_per_sqm': specific_maintenance_cost,
+                              'end_of_life_cost': end_of_life_cost,
+                              'end_of_life_cost_per_sqm': specific_end_of_life_cost,
+                              'operational_income': operational_income,
+                              'operational_income_per_sqm': specific_operational_income,
+                              'total_life_cycle_cost': total_life_cycle_cost,
+                              'total_life_cycle_cost_per_sqm': specific_life_cycle_cost}
+  return life_cycle_cost_analysis

scripts/ep_run_enrich.py

@@ -9,10 +9,10 @@ from hub.imports.results_factory import ResultFactory
 sys.path.append('./')
 
 
-def energy_plus_workflow(city):
+def energy_plus_workflow(city, output_path):
   try:
     # city = city
-    out_path = (Path(__file__).parent.parent / 'out_files')
+    out_path = output_path
     files = glob.glob(f'{out_path}/*')
 
     # for file in files:

scripts/geojson_creator.py

@@ -4,13 +4,16 @@ from shapely import Point
 from pathlib import Path
 
 
-def process_geojson(x, y, diff):
+def process_geojson(x, y, diff, expansion=False):
   selection_box = Polygon([[x + diff, y - diff],
                            [x - diff, y - diff],
                            [x - diff, y + diff],
                            [x + diff, y + diff]])
   geojson_file = Path('./data/collinear_clean 2.geojson').resolve()
-  output_file = Path('./input_files/output_buildings.geojson').resolve()
+  if not expansion:
+    output_file = Path('./input_files/output_buildings.geojson').resolve()
+  else:
+    output_file = Path('./input_files/output_buildings_expanded.geojson').resolve()
   buildings_in_region = []
 
   with open(geojson_file, 'r') as file:

scripts/pv_feasibility.py

@@ -0,0 +1,37 @@
+from pathlib import Path
+import subprocess
+from hub.imports.geometry_factory import GeometryFactory
+from scripts.geojson_creator import process_geojson
+from hub.helpers.dictionaries import Dictionaries
+from hub.imports.weather_factory import WeatherFactory
+from hub.imports.results_factory import ResultFactory
+from hub.exports.exports_factory import ExportsFactory
+
+
+def pv_feasibility(current_x, current_y, current_diff, selected_buildings):
+  input_files_path = (Path(__file__).parent.parent / 'input_files')
+  output_path = (Path(__file__).parent.parent / 'out_files').resolve()
+  sra_output_path = output_path / 'sra_outputs' / 'extended_city_sra_outputs'
+  sra_output_path.mkdir(parents=True, exist_ok=True)
+  new_diff = current_diff * 5
+  geojson_file = process_geojson(x=current_x, y=current_y, diff=new_diff, expansion=True)
+  file_path = input_files_path / 'output_buildings.geojson'
+  city = GeometryFactory('geojson',
+                         path=file_path,
+                         height_field='height',
+                         year_of_construction_field='year_of_construction',
+                         function_field='function',
+                         function_to_hub=Dictionaries().montreal_function_to_hub_function).city
+  WeatherFactory('epw', city).enrich()
+  ExportsFactory('sra', city, sra_output_path).export()
+  sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
+  subprocess.run(['sra', str(sra_path)])
+  ResultFactory('sra', city, sra_output_path).enrich()
+  for selected_building in selected_buildings:
+    for building in city.buildings:
+      if selected_building.name == building.name:
+        selected_building.roofs[0].global_irradiance = building.roofs[0].global_irradiance
+
+
+
+

scripts/random_assignation.py

@@ -15,8 +15,8 @@ from hub.city_model_structure.building import Building
 energy_systems_format = 'montreal_custom'
 
 # parameters:
-residential_systems_percentage = {'system 1 gas': 100,
-                                  'system 1 electricity': 0,
+residential_systems_percentage = {'system 1 gas': 44,
+                                  'system 1 electricity': 6,
                                   'system 2 gas': 0,
                                   'system 2 electricity': 0,
                                   'system 3 and 4 gas': 0,
@@ -25,8 +25,8 @@ residential_systems_percentage = {'system 1 gas': 100,
                                   'system 5 electricity': 0,
                                   'system 6 gas': 0,
                                   'system 6 electricity': 0,
-                                  'system 8 gas': 0,
-                                  'system 8 electricity': 0}
+                                  'system 8 gas': 44,
+                                  'system 8 electricity': 6}
 
 residential_new_systems_percentage = {'PV+ASHP+GasBoiler+TES': 0,
                                       'PV+4Pipe+DHW': 100,

scripts/report_creation.py

@@ -1,73 +1,119 @@
 import subprocess
 import datetime
+import os
+from pathlib import Path
+
 
 class LatexReport:
-  def __init__(self, file_name):
-      self.file_name = file_name
-      self.content = []
-      self.content.append(r'\documentclass{article}')
-      self.content.append(r'\usepackage[margin=2.5cm]{geometry}')  # Adjust page margins
-      self.content.append(r'\usepackage{graphicx}')
-      self.content.append(r'\usepackage{tabularx}')
-      self.content.append(r'\begin{document}')
-      # Get current date and time
-      current_datetime = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-      self.content.append(r'\title{Energy System Analysis Report - ' + current_datetime + r'}')
-      self.content.append(r'\author{Next-Generation Cities Institute}')
-      self.content.append(r'\date{}')  # Remove the date field, as it's included in the title now
-      self.content.append(r'\maketitle')
+  def __init__(self, file_name, title, subtitle, output_path):
+    self.file_name = file_name
+    self.output_path = Path(output_path) / 'report'
+    self.output_path.mkdir(parents=True, exist_ok=True)
+    self.file_path = self.output_path / f"{file_name}.tex"
+    self.content = []
+    self.content.append(r'\documentclass{article}')
+    self.content.append(r'\usepackage[margin=2.5cm]{geometry}')
+    self.content.append(r'\usepackage{graphicx}')
+    self.content.append(r'\usepackage{tabularx}')
+    self.content.append(r'\usepackage{multirow}')
+    self.content.append(r'\usepackage{float}')
+    self.content.append(r'\begin{document}')
+
+    current_datetime = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+    self.content.append(r'\title{' + title + '}')
+    self.content.append(r'\author{Next-Generation Cities Institute}')
+    self.content.append(r'\date{}')
+    self.content.append(r'\maketitle')
+
+    self.content.append(r'\begin{center}')
+    self.content.append(r'\large ' + subtitle + r'\\')
+    self.content.append(r'\large ' + current_datetime)
+    self.content.append(r'\end{center}')
 
   def add_section(self, section_title):
-      self.content.append(r'\section{' + section_title + r'}')
+    self.content.append(r'\section{' + section_title + r'}')
 
   def add_subsection(self, subsection_title):
-      self.content.append(r'\subsection{' + subsection_title + r'}')
+    self.content.append(r'\subsection{' + subsection_title + r'}')
+
+  def add_subsubsection(self, subsection_title):
+    self.content.append(r'\subsubsection{' + subsection_title + r'}')
 
   def add_text(self, text):
-      self.content.append(text)
+    self.content.append(text)
 
-  def add_table(self, table_data, caption=None, first_column_width=None):
+  def add_table(self, table_data, caption=None, first_column_width=None, merge_first_column=False):
     num_columns = len(table_data[0])
-    total_width = 0.9  # Default total width
+    total_width = 0.9
+    first_column_width_str = ''
 
     if first_column_width is not None:
       first_column_width_str = str(first_column_width) + 'cm'
-      total_width -= first_column_width / 16.0  # Adjust total width for the first column
+      total_width -= first_column_width / 16.0
 
     if caption:
       self.content.append(r'\begin{table}[htbp]')
       self.content.append(r'\caption{' + caption + r'}')
       self.content.append(r'\centering')
 
-    self.content.append(r'\begin{tabularx}{\textwidth}{|p{' + first_column_width_str + r'}|' + '|'.join(['X'] * (
-          num_columns - 1)) + '|}' if first_column_width is not None else r'\begin{tabularx}{\textwidth}{|' + '|'.join(
-      ['X'] * num_columns) + '|}')
+    column_format = '|p{' + first_column_width_str + r'}|' + '|'.join(
+      ['X'] * (num_columns - 1)) + '|' if first_column_width is not None else '|' + '|'.join(['X'] * num_columns) + '|'
+    self.content.append(r'\begin{tabularx}{\textwidth}{' + column_format + '}')
     self.content.append(r'\hline')
-    for row in table_data:
-      self.content.append(' & '.join(row) + r' \\')
+
+    previous_first_column = None
+    rowspan_count = 1
+
+    for i, row in enumerate(table_data):
+      if merge_first_column and i > 0 and row[0] == previous_first_column:
+        rowspan_count += 1
+        self.content.append(' & '.join(['' if j == 0 else cell for j, cell in enumerate(row)]) + r' \\')
+      else:
+        if merge_first_column and i > 0 and rowspan_count > 1:
+          self.content[-rowspan_count] = self.content[-rowspan_count].replace(r'\multirow{1}',
+                                                                              r'\multirow{' + str(rowspan_count) + '}')
+          rowspan_count = 1
+        if merge_first_column and i < len(table_data) - 1 and row[0] == table_data[i + 1][0]:
+          self.content.append(r'\multirow{1}{*}{' + row[0] + '}' + ' & ' + ' & '.join(row[1:]) + r' \\')
+        else:
+          self.content.append(' & '.join(row) + r' \\')
+        previous_first_column = row[0]
       self.content.append(r'\hline')
+
+    if merge_first_column and rowspan_count > 1:
+      self.content[-rowspan_count] = self.content[-rowspan_count].replace(r'\multirow{1}',
+                                                                          r'\multirow{' + str(rowspan_count) + '}')
+
     self.content.append(r'\end{tabularx}')
 
     if caption:
       self.content.append(r'\end{table}')
 
-  def add_image(self, image_path, caption=None):
+  def add_image(self, image_path, caption=None, placement='ht'):
     if caption:
-      self.content.append(r'\begin{figure}[htbp]')
+      self.content.append(r'\begin{figure}[' + placement + r']')
       self.content.append(r'\centering')
-      self.content.append(r'\includegraphics[width=0.8\textwidth]{' + image_path + r'}')
+      self.content.append(r'\includegraphics[width=\textwidth]{' + image_path + r'}')
       self.content.append(r'\caption{' + caption + r'}')
       self.content.append(r'\end{figure}')
     else:
-      self.content.append(r'\begin{figure}[htbp]')
+      self.content.append(r'\begin{figure}[' + placement + r']')
       self.content.append(r'\centering')
-      self.content.append(r'\includegraphics[width=0.8\textwidth]{' + image_path + r'}')
+      self.content.append(r'\includegraphics[width=\textwidth]{' + image_path + r'}')
       self.content.append(r'\end{figure}')
 
+  def add_itemize(self, items):
+    self.content.append(r'\begin{itemize}')
+    for item in items:
+      self.content.append(r'\item ' + item)
+    self.content.append(r'\end{itemize}')
+
   def save_report(self):
-    self.content.append(r'\end{document}')  # Add this line to close the document
-    with open(self.file_name, 'w') as f:
+    self.content.append(r'\end{document}')
+    with open(self.file_path, 'w') as f:
       f.write('\n'.join(self.content))
 
   def compile_to_pdf(self):
-      subprocess.run(['pdflatex', self.file_name])
+    subprocess.run(['pdflatex', '-output-directory', str(self.output_path), str(self.file_path)])
+

scripts/system_simulation_models/archetype13.py

@@ -17,8 +17,8 @@ class Archetype13:
     self._heating_peak_load = building.heating_peak_load[cte.YEAR][0]
     self._cooling_peak_load = building.cooling_peak_load[cte.YEAR][0]
     self._domestic_hot_water_peak_load = building.domestic_hot_water_peak_load[cte.YEAR][0]
-    self._hourly_heating_demand = [demand / cte.HOUR_TO_SECONDS for demand in building.heating_demand[cte.HOUR]]
-    self._hourly_cooling_demand = [demand / cte.HOUR_TO_SECONDS for demand in building.cooling_demand[cte.HOUR]]
+    self._hourly_heating_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in building.heating_demand[cte.HOUR]]
+    self._hourly_cooling_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in building.cooling_demand[cte.HOUR]]
     self._hourly_dhw_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in
                                building.domestic_hot_water_heat_demand[cte.HOUR]]
     self._output_path = output_path
@@ -125,11 +125,11 @@ class Archetype13:
         m_dis[i + 1] = 0
         t_ret[i + 1] = t_tank[i + 1]
       else:
-        if demand[i + 1] > 0.5 * self._heating_peak_load / cte.HOUR_TO_SECONDS:
+        if demand[i + 1] > 0.5 * self._heating_peak_load:
           factor = 8
         else:
           factor = 4
-        m_dis[i + 1] = self._heating_peak_load / (cte.WATER_HEAT_CAPACITY * factor * cte.HOUR_TO_SECONDS)
+        m_dis[i + 1] = self._heating_peak_load / (cte.WATER_HEAT_CAPACITY * factor)
         t_ret[i + 1] = t_tank[i + 1] - demand[i + 1] / (m_dis[i + 1] * cte.WATER_HEAT_CAPACITY)
     tes.temperature = []
     hp_electricity_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in hp_electricity]
@@ -191,11 +191,11 @@ class Archetype13:
 
     for i in range(1, len(demand)):
       if demand[i] > 0:
-        m[i] = self._cooling_peak_load / (cte.WATER_HEAT_CAPACITY * 5 * cte.HOUR_TO_SECONDS)
+        m[i] = hp.nominal_cooling_output / (cte.WATER_HEAT_CAPACITY * 5)
         if t_ret[i - 1] >= 13:
-          if demand[i] < 0.25 * self._cooling_peak_load / cte.HOUR_TO_SECONDS:
+          if demand[i] < 0.25 * self._cooling_peak_load:
             q_hp[i] = 0.25 * hp.nominal_cooling_output
-          elif demand[i] < 0.5 * self._cooling_peak_load / cte.HOUR_TO_SECONDS:
+          elif demand[i] < 0.5 * self._cooling_peak_load:
             q_hp[i] = 0.5 * hp.nominal_cooling_output
           else:
             q_hp[i] = hp.nominal_cooling_output
@@ -210,7 +210,7 @@ class Archetype13:
       else:
         m[i] = 0
         q_hp[i] = 0
-        t_sup_hp[i] = t_ret[i -1]
+        t_sup_hp[i] = t_ret[i - 1]
         t_ret[i] = t_ret[i - 1]
       t_sup_hp_fahrenheit = 1.8 * t_sup_hp[i] + 32
       t_out_fahrenheit = 1.8 * t_out[i] + 32
@@ -221,7 +221,7 @@ class Archetype13:
                      eer_curve_coefficients[3] * t_out_fahrenheit +
                      eer_curve_coefficients[4] * t_out_fahrenheit ** 2 +
                      eer_curve_coefficients[5] * t_sup_hp_fahrenheit * t_out_fahrenheit)
-        hp_electricity[i] = q_hp[i] / hp_eer[i]
+        hp_electricity[i] = q_hp[i] / cooling_efficiency
       else:
         hp_eer[i] = 0
         hp_electricity[i] = 0
@@ -377,8 +377,8 @@ class Archetype13:
     self._building.domestic_hot_water_consumption[cte.HOUR] = dhw_consumption
     self._building.domestic_hot_water_consumption[cte.MONTH] = (
       MonthlyValues.get_total_month(self._building.domestic_hot_water_consumption[cte.HOUR]))
-    self._building.domestic_hot_water_consumption[cte.YEAR] = (
-      sum(self._building.domestic_hot_water_consumption[cte.MONTH]))
+    self._building.domestic_hot_water_consumption[cte.YEAR] = [
+      sum(self._building.domestic_hot_water_consumption[cte.MONTH])]
     file_name = f'energy_system_simulation_results_{self._name}.csv'
     with open(self._output_path / file_name, 'w', newline='') as csvfile:
       output_file = csv.writer(csvfile)