20240722 changes

2024-07-17_course_update
Lachine data model is assessed for several building types and U-values
2024-07-22 18:14:54 -04:00 · 2024-07-17 18:39:52 -04:00
13 changed files with 1046 additions and 11871 deletions
--- a/.gitignore
+++ b/.gitignore
@ -11,3 +11,19 @@
 **/.idea/
 cerc_hub.egg-info
 /out_files
+hub/data/construction/nrcan_constructions.json
+hub/helpers/data/hub_function_to_nrcan_construction_function.py
+hub/imports/construction/nrcan_physics_parameters.py
+input_files/Lachine_moved_2019.geojson
+input_files/Lachine_moved_2024.geojson
+input_files/Lachine_moved_existing_year.geojson
+input_files/Lachine_moved_existing_year_type_2.geojson
+input_files/Lachine_moved_existing_year_type_3.geojson
+input_files/district_demand.csv
+input_files/Lachine_moved_2024_type.geojson
+input_files/Lachine_moved_2024_type_2.geojson
+input_files/Lachine_moved_2024_type_3.geojson
+input_files/new_demands_existing_year.xlsx
+input_files/updated_buildings_with_all_data_2024.geojson
+input_files/updated_buildings_with_all_data_baseline.geojson
+.spyproject
--- a/Sup-lot_2023-0428_HYP-BRUTE-90.xlsx
+++ b/Sup-lot_2023-0428_HYP-BRUTE-90.xlsx
--- a/comparing.py
+++ b/comparing.py
@ -0,0 +1,215 @@
+import json
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+# Specify the GeoJSON file path
+input_files_path = (Path(__file__).parent / 'input_files')
+# Load the updated GeoJSON file cotaining the results
+baseline_scenario_path =input_files_path / 'updated_buildings_with_all_data_baseline.geojson'
+energy_efficient_scenario_path = input_files_path / 'updated_buildings_with_all_data_2024.geojson'
+
+with open(baseline_scenario_path , 'r') as f:
+    baseline_scenario = json.load(f)
+with open(energy_efficient_scenario_path, 'r') as f:
+    energy_efficient_scenario=json.load(f)
+percentage_data = {
+    1646: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2672.550473, "total_floor_area": 26725.50473},
+    1647: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2653.626087, "total_floor_area": 26536.26087},
+    1648: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1056.787496, "total_floor_area": 10567.87496},
+    1649: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1906.620746, "total_floor_area": 19066.20746},
+    1650: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 659.1119416, "total_floor_area": 5272.895533},
+    1651: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1167.208109, "total_floor_area": 9337.664871},
+    1652: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1193.251653, "total_floor_area": 9546.013222},
+    1653: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1491.722543, "total_floor_area": 11933.78035},
+    1654: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1168.005028, "total_floor_area": 9344.040224},
+    1655: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1264.906961, "total_floor_area": 10119.25569},
+    1656: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1281.768818, "total_floor_area": 10254.15054},
+    1657: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 290.3886018, "total_floor_area": 2323.108814},
+    1658: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 847.5095193, "total_floor_area": 6780.076155},
+    1659: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1115.319153, "total_floor_area": 8922.553224},
+    1660: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 469.2918062, "total_floor_area": 3754.33445},
+    1661: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1292.298346, "total_floor_area": 10338.38677},
+    1662: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 625.7828863, "total_floor_area": 5006.263091},
+    1663: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1876.02897, "total_floor_area": 15008.23176},
+    1664: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1118.224781, "total_floor_area": 22364.49562},
+    1665: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1502.787808, "total_floor_area": 30055.75617},
+    1666: {"type1_%": 0.891045711, "type2_%": 0.108954289, "type3_%": 0, "roof_area": 3038.486076, "total_floor_area": 30384.86076},
+    1667: {"type1_%": 0.8, "type2_%": 0.2, "type3_%": 0, "roof_area": 1343.832818, "total_floor_area": 13438.32818},
+    1668: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 961.0996956, "total_floor_area": 4805.498478},
+    1669: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 489.1282111, "total_floor_area": 1956.512845},
+    1673: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 1693.141465, "total_floor_area": 5079.424396},
+    1674: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 3248.827576, "total_floor_area": 9746.482729},
+    1675: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 4086.842191, "total_floor_area": 12260.52657},
+    1676: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2786.114146, "total_floor_area": 11144.45658},
+    1677: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 5142.784184, "total_floor_area": 15428.35255},
+    1678: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 6068.664574, "total_floor_area": 18205.99372},
+    1679: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 5646.751407, "total_floor_area": 16940.25422},
+    1680: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 1601.765953, "total_floor_area": 4805.297859},
+    1681: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 9728.221797, "total_floor_area": 29184.66539},
+    1687: {"type1_%": 0.606611029, "type2_%": 0.28211422, "type3_%": 0.11127475, "roof_area": 4268.608743, "total_floor_area": 59760.52241},
+    1688: {"type1_%": 0.92, "type2_%": 0.08, "type3_%": 0, "roof_area": 2146.654828, "total_floor_area": 38639.7869},
+    1689: {"type1_%": 0.96, "type2_%": 0.04, "type3_%": 0, "roof_area": 2860.270711, "total_floor_area": 57205.41421},
+    1690: {"type1_%": 0.94, "type2_%": 0.06, "type3_%": 0, "roof_area": 2189.732519, "total_floor_area": 28466.52275},
+    1691: {"type1_%": 0.75, "type2_%": 0.25, "type3_%": 0, "roof_area": 3159.077523, "total_floor_area": 31590.77523},
+}
+# Define the demand types to be plotted
+demand_types = [
+    'heating_demand_kWh',
+    'cooling_demand_kWh',
+    'domestic_hot_water_heat_demand_kWh',
+    'appliances_electrical_demand_kWh',
+    'lighting_electrical_demand_kWh'
+]
+
+# # Function to extract demand data from the GeoJSON
+# def extract_demand_data(geojson_data, demand_type, period_start, period_end):
+#     demand_data = {}
+#     for feature in geojson_data['features']:
+#         building_id = int(feature['properties']['id'])
+#         if demand_type in feature['properties']:
+#             demand_series = feature['properties'][demand_type][period_start:period_end]
+#             demand_data[building_id] = demand_series
+#     return demand_data
+#
+# # Define time periods
+# period_start_january = 0
+# period_end_january = 168  # First week of January (0-168h)
+# period_start_summer = 3360
+# period_end_summer = 3528  # One week in summer
+#
+# # Function to plot data for all buildings
+# def plot_all_buildings(data, period, title):
+#     plt.figure(figsize=(15, 8))
+#     for building_id, values in data.items():
+#         plt.plot(range(len(values)), values, label=f'Building {building_id}')
+#     plt.xlabel('Hour')
+#     plt.ylabel('Energy Consumption (kWh)')
+#     plt.title(title)
+#     plt.legend(loc='upper right', bbox_to_anchor=(1.15, 1))
+#     plt.show()
+#
+# # Function to compare baseline and efficient scenarios for selected buildings
+# def plot_comparison(selected_buildings, baseline_data, efficient_data, period, title):
+#     plt.figure(figsize=(15, 8))
+#     for building_id in selected_buildings:
+#         baseline = baseline_data.get(building_id, [])
+#         efficient = efficient_data.get(building_id, [])
+#         plt.plot(range(len(baseline)), baseline, label=f'Baseline Building {building_id}')
+#         plt.plot(range(len(efficient)), efficient, linestyle='--', label=f'Efficient Building {building_id}')
+#     plt.xlabel('Hour')
+#     plt.ylabel('Energy Consumption (kWh)')
+#     plt.title(title)
+#     plt.legend(loc='upper right', bbox_to_anchor=(1.15, 1))
+#     plt.show()
+#
+# # Loop through each demand type and extract data, then plot
+# for demand_type in demand_types:
+#     # Extract demand data for the specified periods
+#     baseline_january = extract_demand_data(baseline_scenario, demand_type, period_start_january, period_end_january)
+#     baseline_summer = extract_demand_data(baseline_scenario, demand_type, period_start_summer, period_end_summer)
+#     efficient_january = extract_demand_data(energy_efficient_scenario, demand_type, period_start_january, period_end_january)
+#     efficient_summer = extract_demand_data(energy_efficient_scenario, demand_type, period_start_summer, period_end_summer)
+#
+#     # Plot all buildings for the first week of January and one week in summer
+#     plot_all_buildings(baseline_january, range(period_start_january, period_end_january), f'First Week of January - Baseline Scenario - {demand_type}')
+#     plot_all_buildings(baseline_summer, range(period_start_summer, period_end_summer), f'One Week in Summer - Baseline Scenario - {demand_type}')
+#
+#     # Selected buildings for comparison
+#     selected_buildings = [1646, 1691, 1687]
+#
+#     # Plot comparison for the first week of January and one week in summer
+#     plot_comparison(selected_buildings, baseline_january, efficient_january, range(period_start_january, period_end_january), f'First Week of January - Comparison - {demand_type}')
+#     plot_comparison(selected_buildings, baseline_summer, efficient_summer, range(period_start_summer, period_end_summer), f'One Week in Summer - Comparison - {demand_type}')
+#
+
+
+# Function to extract and sum demand data from the GeoJSON
+def extract_and_sum_demand_data(geojson_data, demand_type, period_start, period_end):
+    district_demand = [0] * (period_end - period_start)
+    for feature in geojson_data['features']:
+        if demand_type in feature['properties']:
+            demand_series = feature['properties'][demand_type][period_start:period_end]
+            district_demand = [sum(x) for x in zip(district_demand, demand_series)]
+    # Convert kWh to MWh
+    district_demand_mwh = [value / 1000 for value in district_demand]
+    return district_demand_mwh
+
+# Define time periods
+period_start_january = 0
+period_end_january = 168  # First week of January (0-168h)
+period_start_summer = 3360
+period_end_summer = 3528  # One week in summer
+
+# Function to plot district demand
+def plot_district_demand(district_demand, title):
+    plt.figure(figsize=(15, 8))
+    plt.plot(range(len(district_demand)), district_demand, label='District Demand')
+    plt.xlabel('Hour')
+    plt.ylabel('Energy Consumption (MWh)')
+    plt.title(title)
+    plt.legend(loc='upper right')
+    plt.show()
+
+# Function to compare district demands between baseline and efficient scenarios
+def plot_district_comparison(district_demand_baseline, district_demand_efficient, title):
+    plt.figure(figsize=(15, 8))
+    plt.plot(range(len(district_demand_baseline)), district_demand_baseline, label='Baseline District Demand')
+    plt.plot(range(len(district_demand_efficient)), district_demand_efficient, linestyle='--', label='Efficient District Demand')
+    plt.xlabel('Hour')
+    plt.ylabel('Energy Consumption (MWh)')
+    plt.title(title)
+    plt.legend(loc='upper right')
+    plt.show()
+
+# # Loop through each demand type, extract and sum data, then plot
+# for demand_type in demand_types:
+#     # Extract and sum demand data for the specified periods
+#     district_demand_january_baseline = extract_and_sum_demand_data(baseline_scenario, demand_type, period_start_january, period_end_january)
+#     district_demand_summer_baseline = extract_and_sum_demand_data(baseline_scenario, demand_type, period_start_summer, period_end_summer)
+#     district_demand_january_efficient = extract_and_sum_demand_data(energy_efficient_scenario, demand_type, period_start_january, period_end_january)
+#     district_demand_summer_efficient = extract_and_sum_demand_data(energy_efficient_scenario, demand_type, period_start_summer, period_end_summer)
+#
+#     # Plot district demand for the first week of January and one week in summer for baseline scenario
+#     plot_district_demand(district_demand_january_baseline, f'District Demand - First Week of January - Baseline Scenario - {demand_type}')
+#     plot_district_demand(district_demand_summer_baseline, f'District Demand - One Week in Summer - Baseline Scenario - {demand_type}')
+#
+#     # Plot district demand for the first week of January and one week in summer for energy-efficient scenario
+#     plot_district_demand(district_demand_january_efficient, f'District Demand - First Week of January - Energy Efficient Scenario - {demand_type}')
+#     plot_district_demand(district_demand_summer_efficient, f'District Demand - One Week in Summer - Energy Efficient Scenario - {demand_type}')
+#
+#     # Plot comparison of district demand between baseline and energy-efficient scenarios
+#     plot_district_comparison(district_demand_january_baseline, district_demand_january_efficient, f'District Demand Comparison - First Week of January - {demand_type}')
+#     plot_district_comparison(district_demand_summer_baseline, district_demand_summer_efficient, f'District Demand Comparison - One Week in Summer - {demand_type}')
+# Function to extract and sum demand data from the GeoJSON
+# Define time period for a year
+period_start = 0
+period_end = 8760  # Full year (0-8760h)
+
+# Dictionary to store the district demand data for export
+district_demand_data = {'hour': list(range(period_start, period_end))}
+import pandas as pd
+# Loop through each demand type, extract and sum data, then store in dictionary
+for demand_type in demand_types:
+    # Extract and sum demand data for the full year
+    district_demand_mwh_baseline = extract_and_sum_demand_data(baseline_scenario, demand_type, period_start, period_end)
+    district_demand_mwh_efficient = extract_and_sum_demand_data(energy_efficient_scenario, demand_type, period_start,
+                                                                period_end)
+
+    # Store data in the dictionary
+    district_demand_data[f'{demand_type}_baseline'] = district_demand_mwh_baseline
+    district_demand_data[f'{demand_type}_efficient'] = district_demand_mwh_efficient
+
+    # Calculate total district demand values for the year
+    total_district_baseline = sum(district_demand_mwh_baseline)
+    total_district_efficient = sum(district_demand_mwh_efficient)
+
+    # Print total district demand values
+    print(f'Total District Demand for {demand_type} (Baseline): {total_district_baseline:.2f} MWh')
+    print(f'Total District Demand for {demand_type} (Efficient): {total_district_efficient:.2f} MWh')
+
+# Convert dictionary to DataFrame and export to CSV
+district_demand_df = pd.DataFrame(district_demand_data)
+output_csv_path = input_files_path / 'district_demand.csv'
+district_demand_df.to_csv(output_csv_path, index=False)
+
+print(f'District demand data exported to {output_csv_path}')
--- a/hub/city_model_structure/building.py
+++ b/hub/city_model_structure/building.py
@ -415,8 +415,8 @@ class Building(CityObject):
        peak = max(schedule.values) * lighting.density * thermal_zone.total_floor_area
        if peak > peak_lighting:
          peak_lighting = peak
-    results[cte.MONTH] = [peak for _ in range(0, 12)]
-    results[cte.YEAR] = [peak]
+    results[cte.MONTH] = [float(peak) for _ in range(0, 12)]
+    results[cte.YEAR] = [float(peak)]
    return results

  @property
@ -434,8 +434,8 @@ class Building(CityObject):
        peak = max(schedule.values) * appliances.density * thermal_zone.total_floor_area
        if peak > peak_appliances:
          peak_appliances = peak
-    results[cte.MONTH] = [peak for _ in range(0, 12)]
-    results[cte.YEAR] = [peak]
+    results[cte.MONTH] = [float(peak) for _ in range(0, 12)]
+    results[cte.YEAR] = [float(peak)]
    return results

  @property
--- a/hub/data/construction/nrcan_constructions.json
+++ b/hub/data/construction/nrcan_constructions.json
--- a/hub/helpers/data/hub_function_to_nrcan_construction_function.py
+++ b/hub/helpers/data/hub_function_to_nrcan_construction_function.py
@ -1,86 +0,0 @@
-"""
-Dictionaries module for hub function to nrcan construction function
-SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2022 Concordia CERC group
-Project Coder Guille Gutierrez Guillermo.GutierrezMorote@concordia.ca
-"""
-
-import hub.helpers.constants as cte
-
-
-class HubFunctionToNrcanConstructionFunction:
-  """
-  Hub function to nrcan construction function class
-  """
-  def __init__(self):
-    self._dictionary = {
-      cte.RESIDENTIAL: 'MidriseApartment',
-      cte.SINGLE_FAMILY_HOUSE: 'MidriseApartment',
-      cte.MULTI_FAMILY_HOUSE: 'HighriseApartment',
-      cte.ROW_HOUSE: 'MidriseApartment',
-      cte.MID_RISE_APARTMENT: 'MidriseApartment',
-      cte.HIGH_RISE_APARTMENT: 'HighriseApartment',
-      cte.OFFICE_AND_ADMINISTRATION: 'MediumOffice',
-      cte.SMALL_OFFICE: 'SmallOffice',
-      cte.MEDIUM_OFFICE: 'MediumOffice',
-      cte.LARGE_OFFICE: 'LargeOffice',
-      cte.COURTHOUSE: 'MediumOffice',
-      cte.FIRE_STATION: 'n/a',
-      cte.PENITENTIARY: 'LargeHotel',
-      cte.POLICE_STATION: 'n/a',
-      cte.POST_OFFICE: 'MediumOffice',
-      cte.LIBRARY: 'MediumOffice',
-      cte.EDUCATION: 'SecondarySchool',
-      cte.PRIMARY_SCHOOL: 'PrimarySchool',
-      cte.PRIMARY_SCHOOL_WITH_SHOWER: 'PrimarySchool',
-      cte.SECONDARY_SCHOOL: 'SecondarySchool',
-      cte.UNIVERSITY: 'SecondarySchool',
-      cte.LABORATORY_AND_RESEARCH_CENTER: 'SecondarySchool',
-      cte.STAND_ALONE_RETAIL: 'RetailStandalone',
-      cte.HOSPITAL: 'Hospital',
-      cte.OUT_PATIENT_HEALTH_CARE: 'Outpatient',
-      cte.HEALTH_CARE: 'Outpatient',
-      cte.RETIREMENT_HOME_OR_ORPHANAGE: 'SmallHotel',
-      cte.COMMERCIAL: 'RetailStripmall',
-      cte.STRIP_MALL: 'RetailStripmall',
-      cte.SUPERMARKET: 'RetailStripmall',
-      cte.RETAIL_SHOP_WITHOUT_REFRIGERATED_FOOD: 'RetailStandalone',
-      cte.RETAIL_SHOP_WITH_REFRIGERATED_FOOD: 'RetailStandalone',
-      cte.RESTAURANT: 'FullServiceRestaurant',
-      cte.QUICK_SERVICE_RESTAURANT: 'QuickServiceRestaurant',
-      cte.FULL_SERVICE_RESTAURANT: 'FullServiceRestaurant',
-      cte.HOTEL: 'SmallHotel',
-      cte.HOTEL_MEDIUM_CLASS: 'SmallHotel',
-      cte.SMALL_HOTEL: 'SmallHotel',
-      cte.LARGE_HOTEL: 'LargeHotel',
-      cte.DORMITORY: 'SmallHotel',
-      cte.EVENT_LOCATION: 'n/a',
-      cte.CONVENTION_CENTER: 'n/a',
-      cte.HALL: 'n/a',
-      cte.GREEN_HOUSE: 'n/a',
-      cte.INDUSTRY: 'n/a',
-      cte.WORKSHOP: 'n/a',
-      cte.WAREHOUSE: 'Warehouse',
-      cte.WAREHOUSE_REFRIGERATED: 'Warehouse',
-      cte.SPORTS_LOCATION: 'n/a',
-      cte.SPORTS_ARENA: 'n/a',
-      cte.GYMNASIUM: 'n/a',
-      cte.MOTION_PICTURE_THEATRE: 'n/a',
-      cte.MUSEUM: 'n/a',
-      cte.PERFORMING_ARTS_THEATRE: 'n/a',
-      cte.TRANSPORTATION: 'n/a',
-      cte.AUTOMOTIVE_FACILITY: 'n/a',
-      cte.PARKING_GARAGE: 'n/a',
-      cte.RELIGIOUS: 'n/a',
-      cte.NON_HEATED: 'n/a',
-      cte.DATACENTER: 'n/a',
-      cte.FARM: 'n/a'
-    }
-
-  @property
-  def dictionary(self) -> dict:
-    """
-    Get the dictionary
-    :return: {}
-    """
-    return self._dictionary
--- a/hub/imports/construction/nrcan_physics_parameters.py
+++ b/hub/imports/construction/nrcan_physics_parameters.py
@ -1,105 +0,0 @@
-"""
-NrcanPhysicsParameters import the construction and material information defined by NRCAN
-SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2022 Concordia CERC group
-Project Coder Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
-"""
-
-import logging
-
-from hub.catalog_factories.construction_catalog_factory import ConstructionCatalogFactory
-from hub.city_model_structure.building_demand.thermal_archetype import ThermalArchetype
-from hub.city_model_structure.building_demand.construction import Construction
-from hub.city_model_structure.building_demand.layer import Layer
-from hub.helpers.dictionaries import Dictionaries
-from hub.imports.construction.helpers.construction_helper import ConstructionHelper
-
-
-class NrcanPhysicsParameters:
-  """
-  NrcanPhysicsParameters class
-  """
-
-  def __init__(self, city, divide_in_storeys=False):
-    self._city = city
-    self._divide_in_storeys = divide_in_storeys
-    self._climate_zone = ConstructionHelper.city_to_nrcan_climate_zone(city.climate_reference_city)
-
-  def enrich_buildings(self):
-    """
-    Returns the city with the construction parameters assigned to the buildings
-    """
-    city = self._city
-    nrcan_catalog = ConstructionCatalogFactory('nrcan').catalog
-    for building in city.buildings:
-      if building.function not in Dictionaries().hub_function_to_nrcan_construction_function:
-        logging.error('Building %s has an unknown building function %s', building.name, building.function)
-        continue
-      function = Dictionaries().hub_function_to_nrcan_construction_function[building.function]
-      try:
-        archetype = self._search_archetype(nrcan_catalog, function, building.year_of_construction, self._climate_zone)
-
-      except KeyError:
-        logging.error('Building %s has unknown construction archetype for building function: %s '
-                      '[%s], building year of construction: %s and climate zone %s', building.name, function,
-                      building.function, building.year_of_construction, self._climate_zone)
-        continue
-      thermal_archetype = ThermalArchetype()
-      self._assign_values(thermal_archetype, archetype)
-      for internal_zone in building.internal_zones:
-        internal_zone.thermal_archetype = thermal_archetype
-
-  @staticmethod
-  def _search_archetype(nrcan_catalog, function, year_of_construction, climate_zone):
-    nrcan_archetypes = nrcan_catalog.entries('archetypes')
-    for building_archetype in nrcan_archetypes:
-      construction_period_limits = building_archetype.construction_period.split('_')
-      if int(construction_period_limits[0]) <= int(year_of_construction) <= int(construction_period_limits[1]):
-        if str(function) == str(building_archetype.function) and climate_zone == str(building_archetype.climate_zone):
-          return building_archetype
-    raise KeyError('archetype not found')
-
-  @staticmethod
-  def _assign_values(thermal_archetype, catalog_archetype):
-    thermal_archetype.average_storey_height = catalog_archetype.average_storey_height
-    thermal_archetype.extra_loses_due_to_thermal_bridges = catalog_archetype.extra_loses_due_to_thermal_bridges
-    thermal_archetype.thermal_capacity = catalog_archetype.thermal_capacity
-    thermal_archetype.indirect_heated_ratio = 0
-    thermal_archetype.infiltration_rate_for_ventilation_system_on = catalog_archetype.infiltration_rate_for_ventilation_system_on
-    thermal_archetype.infiltration_rate_for_ventilation_system_off = catalog_archetype.infiltration_rate_for_ventilation_system_off
-    _constructions = []
-    for catalog_construction in catalog_archetype.constructions:
-      construction = Construction()
-      construction.type = catalog_construction.type
-      construction.name = catalog_construction.name
-      if catalog_construction.window_ratio is not None:
-        for _orientation in catalog_construction.window_ratio:
-          if catalog_construction.window_ratio[_orientation] is None:
-            catalog_construction.window_ratio[_orientation] = 0
-      construction.window_ratio = catalog_construction.window_ratio
-      _layers = []
-      for layer_archetype in catalog_construction.layers:
-        layer = Layer()
-        layer.thickness = layer_archetype.thickness
-        archetype_material = layer_archetype.material
-        layer.material_name = archetype_material.name
-        layer.no_mass = archetype_material.no_mass
-        if archetype_material.no_mass:
-          layer.thermal_resistance = archetype_material.thermal_resistance
-        else:
-          layer.density = archetype_material.density
-          layer.conductivity = archetype_material.conductivity
-          layer.specific_heat = archetype_material.specific_heat
-        layer.solar_absorptance = archetype_material.solar_absorptance
-        layer.thermal_absorptance = archetype_material.thermal_absorptance
-        layer.visible_absorptance = archetype_material.visible_absorptance
-        _layers.append(layer)
-      construction.layers = _layers
-
-      if catalog_construction.window is not None:
-        window_archetype = catalog_construction.window
-        construction.window_frame_ratio = window_archetype.frame_ratio
-        construction.window_g_value = window_archetype.g_value
-        construction.window_overall_u_value = window_archetype.overall_u_value
-      _constructions.append(construction)
-    thermal_archetype.constructions = _constructions
--- a/hub/imports/geometry/geojson.py
+++ b/hub/imports/geometry/geojson.py
@ -135,6 +135,8 @@ class Geojson:
        building_aliases = []
        if 'id' in feature:
          building_name = feature['id']
+        elif 'id' in feature['properties']:
+          building_name = feature['properties']['id']
        else:
          building_name = uuid.uuid4()
        if self._aliases_field is not None:
--- a/input_files/Lachine_moved_existing_year_type.geojson
+++ b/input_files/Lachine_moved_existing_year_type.geojson
@ -0,0 +1,45 @@
+{
+"type": "FeatureCollection",
+"name": "Lachine_moved_existing_year_type",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
+"features": [
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+{ "type": "Feature", "properties": { "id": "1690", "footprint_area": 1674.9280842908665, "floor_height": 4.0, "year_built": 2019, "detailed_model_filename": "C:\\Users\\umroot\\Downloads\\Group 3\\hb_json\\Building_1690_0146d7fc.json", "type": "Building", "building_type": "1000", "number_of_stories": 10, "name": "Building_1690", "number_of_stories_above_ground": 10, "floor_area": 16749.280842908665, "maximum_roof_height": 40.0, "building_type_2": 6111, "building_type_3": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -73.668126534542566, 45.437681866271078 ], [ -73.668383377125423, 45.437633845683195 ], [ -73.668383491801876, 45.437217551516433 ], [ -73.668126409436084, 45.437217643643855 ], [ -73.668126534542566, 45.437523541871258 ], [ -73.668023824027188, 45.437542745148519 ], [ -73.668023824027188, 45.437514805548552 ], [ -73.66766413614485, 45.437582054613621 ], [ -73.66766415445214, 45.437768315190588 ], [ -73.668023765788789, 45.437701080436888 ], [ -73.668023824027188, 45.437673129948379 ], [ -73.6681266029602, 45.437653913879537 ], [ -73.668126534542566, 45.437681866271078 ] ] ] } },
+{ "type": "Feature", "properties": { "id": "1691", "footprint_area": 2416.2204562020706, "floor_height": 3.9999999999999996, "year_built": 2019, "detailed_model_filename": "C:\\Users\\umroot\\Downloads\\Group 3\\hb_json\\Building_1691_e4a64903.json", "type": "Building", "building_type": "1000", "number_of_stories": 8, "name": "Building_1691", "number_of_stories_above_ground": 8, "floor_area": 19329.763649616565, "maximum_roof_height": 31.999999999999996, "building_type_2": 6111, "building_type_3": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -73.66765675225426, 45.435369763356881 ], [ -73.667656760492804, 45.435368747291285 ], [ -73.668226081270987, 45.435369758924502 ], [ -73.668226073032457, 45.435310418637101 ], [ -73.668225335766905, 45.435310418637101 ], [ -73.668225335766905, 45.435220718797545 ], [ -73.668226073032457, 45.435220718797545 ], [ -73.66842324930586, 45.435220707215464 ], [ -73.66842268222733, 45.435175714511374 ], [ -73.668422683258228, 45.434982523102775 ], [ -73.667606057102702, 45.434982225122766 ], [ -73.667606057102702, 45.435220703648262 ], [ -73.66765675225426, 45.435220718797545 ], [ -73.66765675225426, 45.435369763356881 ] ] ] } }
+]
+}
--- a/main.py
+++ b/main.py
@ -7,9 +7,31 @@ from hub.imports.usage_factory import UsageFactory
 from hub.imports.weather_factory import WeatherFactory
 import hub.helpers.constants as cte
 from hub.imports.energy_systems_factory import EnergySystemsFactory
+import geopandas as gpd
 # Specify the GeoJSON file path
 input_files_path = (Path(__file__).parent / 'input_files')
-geojson_file_path = input_files_path / 'test.geojson'
+
+building_type_2_modelling=2
+#'Lachine_New_Developments.geojson'
+geojson_file_path = input_files_path / 'Lachine_moved_2024_type.geojson'
+if building_type_2_modelling==1:
+    gdf = gpd.read_file(geojson_file_path)
+    # Filter gdf when 'building_type_2' is not null
+    filtered_gdf = gdf[gdf['building_type_2'].notnull()]
+    output_geojson =input_files_path /'Lachine_moved_2024_type_2.geojson'
+    geojson_file_path =output_geojson
+    filtered_gdf.to_file(output_geojson, driver='GeoJSON')
+    print(f"New GeoJSON saved in: {output_geojson}")
+if building_type_2_modelling==2:
+    gdf = gpd.read_file(geojson_file_path)
+    # Filter gdf when 'building_type_3' is not null
+    filtered_gdf = gdf[gdf['building_type_3'].notnull()]
+    output_geojson =input_files_path /'Lachine_moved_2024_type_3.geojson'
+    geojson_file_path = output_geojson
+    filtered_gdf.to_file(output_geojson, driver='GeoJSON')
+    print(f"New GeoJSON saved in: {output_geojson}")
+
+
 output_path = (Path(__file__).parent / 'out_files').resolve()
 output_path.mkdir(parents=True, exist_ok=True)
 # Create city object from GeoJSON file
@ -24,34 +46,114 @@ ConstructionFactory('nrcan', city).enrich()
 UsageFactory('nrcan', city).enrich()
 WeatherFactory('epw', city).enrich()
 energy_plus_workflow(city)
+def to_dict(building, total_floor_area):
+    return {
+        'roof_area': building.floor_area,
+        'total_floor_area': total_floor_area,
+        'year_of_construction' : building.year_of_construction,
+        'type_function':building.function,
+        'beam_kWh_per_m2': sum(building.beam[cte.HOUR])/ (3.6e6),
+        'diffuse_kWh_per_m2': sum(building.diffuse[cte.HOUR])/ (3.6e6),
+        'direct_normal_kWh_per_m2': sum(building.direct_normal[cte.HOUR])/ (3.6e6),
+        'average_storey_height_meters': building.average_storey_height,
+        'max_height_meters_meters': building.max_height,
+        'global_horizontal_kWh_per_m2': sum(building.global_horizontal[cte.HOUR])/ (3.6e6),
+        'appliances_peak_load_kW':building.appliances_peak_load[cte.YEAR][0]/ (1e3),
+        'domestic_hot_water_peak_load_kW': building.domestic_hot_water_peak_load[cte.YEAR][0]/ (1e3),
+        'heating_peak_load_kW': building.heating_peak_load[cte.YEAR][0]/ (1e3),
+        'cooling_peak_load_kW': building.cooling_peak_load[cte.YEAR][0]/ (1e3),
+        'lighting_peak_load_kW': building.lighting_peak_load[cte.YEAR][0]/ (1e3),
+        'heating_demand_kWh_per_m2' : sum(building.heating_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
+        'cooling_demand_kWh_per_m2' : sum(building.cooling_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
+        'domestic_hot_water_heat_demand_kWh_per_m2': sum(building.domestic_hot_water_heat_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
+        'appliances_electrical_demand_kWh_per_m2':sum(building.appliances_electrical_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
+        'lighting_electrical_demand_kWh_per_m2': sum(building.lighting_electrical_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
+        'heating_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.heating_demand[cte.HOUR]],
+        'cooling_demand_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in  building.cooling_demand[cte.HOUR]],
+        'domestic_hot_water_heat_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.domestic_hot_water_heat_demand[cte.HOUR]],
+        'appliances_electrical_demand_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in  building.appliances_electrical_demand[cte.HOUR]],
+        'lighting_electrical_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.lighting_electrical_demand[cte.HOUR]]
+    }
+buildings_dic={}
+
+
 for building in city.buildings:
-  print(building.heating_demand[cte.YEAR][0] / 3.6e6)
-  print(building.name)
-  total_floor_area = 0
-  for thermal_zone in building.thermal_zones_from_internal_zones:
-    total_floor_area += thermal_zone.total_floor_area
-  print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
-  building.energy_systems_archetype_name = 'system 1 gas'
+
+    total_floor_area = 0
+    for thermal_zone in building.thermal_zones_from_internal_zones:
+        total_floor_area += thermal_zone.total_floor_area
+        print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
+        building.energy_systems_archetype_name = 'system 1 gas'
+        buildings_dic[building.name] = to_dict(building,total_floor_area)
 print('test')
-EnergySystemsFactory('montreal_custom', city).enrich()
-print('test')
-for building in city.buildings:
-  energy_systems = building.energy_systems
-  for energy_system in energy_systems:
-    generation_units = energy_system.generation_systems
-    if cte.HEATING in energy_system.demand_types:
-      for generation_unit in generation_units:
-        generation_unit.heat_efficiency = 0.96
+
+""""EXPORTERS"""
+import pandas as pd
+# Convert the dictionary to a DataFrame
+df = pd.DataFrame.from_dict(buildings_dic, orient='index')
+
+# Export the DataFrame to an Excel file
+excel_file_path = r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\buildings.xlsx'
+df.to_excel(excel_file_path, index=True, index_label='Building')
+
+import json
+
+
+def make_json_serializable(data):
+    if isinstance(data, (str, int, float, bool, type(None))):
+        return data
+    elif isinstance(data, dict):
+        return {k: make_json_serializable(v) for k, v in data.items()}
+    elif isinstance(data, list):
+        return [make_json_serializable(item) for item in data]
+    else:
+        return str(data)  # Convert any other type to a string
+
+# Load the existing GeoJSON file
+
+with open(geojson_file_path, 'r') as f:
+    geojson_data = json.load(f)
+
+# Update the properties of each feature
+for feature in geojson_data['features']:
+    # Attempt to retrieve the building_id from 'id' or 'properties'
+    building_id = feature.get('id') or feature['properties'].get('id')
+
+    if building_id in buildings_dic:
+        serializable_properties = make_json_serializable(buildings_dic[building_id])
+        feature['properties'].update(serializable_properties)
+
+# Save the updated GeoJSON to a new file
+updated_geojson_file_path = r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\updated_buildings.geojson'  # Replace with your actual path
+with open(updated_geojson_file_path, 'w') as f:
+    json.dump(geojson_data, f, indent=4)
+
+
+# EnergySystemsFactory('montreal_custom', city).enrich()
+# print('test')
 # for building in city.buildings:
-#   building.function = cte.COMMERCIAL
-#
-# ConstructionFactory('nrcan', city).enrich()
-# UsageFactory('nrcan', city).enrich()
-# energy_plus_workflow(city)
+#   energy_systems = building.energy_systems
+#   for energy_system in energy_systems:
+#     generation_units = energy_system.generation_systems
+#     if cte.HEATING in energy_system.demand_types:
+#       for generation_unit in generation_units:
+#         generation_unit.heat_efficiency = 0.96
+# # for building in city.buildings:
+# #   building.function = cte.COMMERCIAL
+# #
+# # ConstructionFactory('nrcan', city).enrich()
+# # UsageFactory('nrcan', city).enrich()
+# # energy_plus_workflow(city)
+# # for building in city.buildings:
+# #   print(building.heating_demand[cte.YEAR][0] / 3.6e6)
+# #   print(building.name)
+# #   total_floor_area = 0
+# #   for thermal_zone in building.thermal_zones_from_internal_zones:
+# #     total_floor_area += thermal_zone.total_floor_area
+# #   print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
+
+
 # for building in city.buildings:
-#   print(building.heating_demand[cte.YEAR][0] / 3.6e6)
-#   print(building.name)
-#   total_floor_area = 0
-#   for thermal_zone in building.thermal_zones_from_internal_zones:
-#     total_floor_area += thermal_zone.total_floor_area
-#   print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
+#     print(building.name)
+#     print(building.year_of_construction)
+#     print(building.usages_percentage)
--- a/main_results_in_geojson.py
+++ b/main_results_in_geojson.py
@ -0,0 +1,176 @@
+from pathlib import Path
+from scripts.ep_workflow import energy_plus_workflow
+from hub.helpers.monthly_values import MonthlyValues
+from hub.imports.geometry_factory import GeometryFactory
+from hub.helpers.dictionaries import Dictionaries
+from hub.imports.construction_factory import ConstructionFactory
+from hub.imports.usage_factory import UsageFactory
+from hub.imports.weather_factory import WeatherFactory
+import hub.helpers.constants as cte
+from hub.imports.energy_systems_factory import EnergySystemsFactory
+from hub.helpers.peak_loads import PeakLoads
+
+import geopandas as gpd
+import json
+# Specify the GeoJSON file path
+input_files_path = (Path(__file__).parent / 'input_files')
+output_path = (Path(__file__).parent / 'out_files').resolve()
+output_path.mkdir(parents=True, exist_ok=True)
+
+geojson_file_path = output_path / 'updated_buildings_with_all_data.geojson'
+with open(geojson_file_path , 'r') as f:
+    building_type_data = json.load(f)
+    
+
+
+# Create city object from GeoJSON file
+city = GeometryFactory('geojson',
+                       path=geojson_file_path,
+                       height_field='maximum_roof_height',
+                       year_of_construction_field='year_built',
+                       function_field='building_type',
+                       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()
+
+# #energy plus is not going to be processed here, as demand has been obtained before
+# energy_plus_workflow(city)
+percentage_data = {
+    1646: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2672.550473, "total_floor_area": 26725.50473},
+    1647: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2653.626087, "total_floor_area": 26536.26087},
+    1648: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1056.787496, "total_floor_area": 10567.87496},
+    1649: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1906.620746, "total_floor_area": 19066.20746},
+    1650: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 659.1119416, "total_floor_area": 5272.895533},
+    1651: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1167.208109, "total_floor_area": 9337.664871},
+    1652: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1193.251653, "total_floor_area": 9546.013222},
+    1653: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1491.722543, "total_floor_area": 11933.78035},
+    1654: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1168.005028, "total_floor_area": 9344.040224},
+    1655: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1264.906961, "total_floor_area": 10119.25569},
+    1656: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1281.768818, "total_floor_area": 10254.15054},
+    1657: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 290.3886018, "total_floor_area": 2323.108814},
+    1658: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 847.5095193, "total_floor_area": 6780.076155},
+    1659: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1115.319153, "total_floor_area": 8922.553224},
+    1660: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 469.2918062, "total_floor_area": 3754.33445},
+    1661: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1292.298346, "total_floor_area": 10338.38677},
+    1662: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 625.7828863, "total_floor_area": 5006.263091},
+    1663: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1876.02897, "total_floor_area": 15008.23176},
+    1664: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1118.224781, "total_floor_area": 22364.49562},
+    1665: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1502.787808, "total_floor_area": 30055.75617},
+    1666: {"type1_%": 0.891045711, "type2_%": 0.108954289, "type3_%": 0, "roof_area": 3038.486076, "total_floor_area": 30384.86076},
+    1667: {"type1_%": 0.8, "type2_%": 0.2, "type3_%": 0, "roof_area": 1343.832818, "total_floor_area": 13438.32818},
+    1668: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 961.0996956, "total_floor_area": 4805.498478},
+    1669: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 489.1282111, "total_floor_area": 1956.512845},
+    1673: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 1693.141465, "total_floor_area": 5079.424396},
+    1674: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 3248.827576, "total_floor_area": 9746.482729},
+    1675: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 4086.842191, "total_floor_area": 12260.52657},
+    1676: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2786.114146, "total_floor_area": 11144.45658},
+    1677: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 5142.784184, "total_floor_area": 15428.35255},
+    1678: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 6068.664574, "total_floor_area": 18205.99372},
+    1679: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 5646.751407, "total_floor_area": 16940.25422},
+    1680: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 1601.765953, "total_floor_area": 4805.297859},
+    1681: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 9728.221797, "total_floor_area": 29184.66539},
+    1687: {"type1_%": 0.606611029, "type2_%": 0.28211422, "type3_%": 0.11127475, "roof_area": 4268.608743, "total_floor_area": 59760.52241},
+    1688: {"type1_%": 0.92, "type2_%": 0.08, "type3_%": 0, "roof_area": 2146.654828, "total_floor_area": 38639.7869},
+    1689: {"type1_%": 0.96, "type2_%": 0.04, "type3_%": 0, "roof_area": 2860.270711, "total_floor_area": 57205.41421},
+    1690: {"type1_%": 0.94, "type2_%": 0.06, "type3_%": 0, "roof_area": 2189.732519, "total_floor_area": 28466.52275},
+    1691: {"type1_%": 0.75, "type2_%": 0.25, "type3_%": 0, "roof_area": 3159.077523, "total_floor_area": 31590.77523},
+}
+
+def enrich_buildings_with_geojson_data (building_type_data, city):
+      for building in city.buildings:
+          for idx, feature in enumerate(building_type_data['features']):
+              if feature['properties']['id'] == str(building.name):
+                  building.heating_demand[cte.HOUR] = [x *1000* cte.WATTS_HOUR_TO_JULES for x in building_type_data['features'][idx]['properties'].get('heating_demand_kWh', [0])]
+                  building.cooling_demand[cte.HOUR] = [x *1000* cte.WATTS_HOUR_TO_JULES for x in building_type_data['features'][idx]['properties'].get('cooling_demand_kWh', [0])]
+                  building.domestic_hot_water_heat_demand[cte.HOUR] =    [x *1000* cte.WATTS_HOUR_TO_JULES for x in building_type_data['features'][idx]['properties'].get('domestic_hot_water_heat_demand_kWh', [0])]
+                  building.appliances_electrical_demand[cte.HOUR] =   [x *1000* cte.WATTS_HOUR_TO_JULES for x in building_type_data['features'][idx]['properties'].get('appliances_electrical_demand_kWh', [0])]
+                  building.lighting_electrical_demand[cte.HOUR] =   [x *1000* cte.WATTS_HOUR_TO_JULES for x in building_type_data['features'][idx]['properties'].get('lighting_electrical_demand_kWh', [0])]
+                  building.heating_demand[cte.MONTH] = MonthlyValues.get_total_month(building.heating_demand[cte.HOUR])
+                  building.cooling_demand[cte.MONTH] = MonthlyValues.get_total_month(building.cooling_demand[cte.HOUR])
+                  building.domestic_hot_water_heat_demand[cte.MONTH] = (MonthlyValues.get_total_month(building.domestic_hot_water_heat_demand[cte.HOUR]))
+                  building.appliances_electrical_demand[cte.MONTH] = (MonthlyValues.get_total_month(building.appliances_electrical_demand[cte.HOUR]))
+                  building.lighting_electrical_demand[cte.MONTH] = (MonthlyValues.get_total_month(building.lighting_electrical_demand[cte.HOUR]))
+                  building.heating_demand[cte.YEAR] = [sum(building.heating_demand[cte.MONTH])]
+                  building.cooling_demand[cte.YEAR] = [sum(building.cooling_demand[cte.MONTH])]
+                  building.domestic_hot_water_heat_demand[cte.YEAR] = [sum(building.domestic_hot_water_heat_demand[cte.MONTH])]
+                  building.appliances_electrical_demand[cte.YEAR] = [sum(building.appliances_electrical_demand[cte.MONTH])]
+                  building.lighting_electrical_demand[cte.YEAR] = [sum(building.lighting_electrical_demand[cte.MONTH])]
+
+
+
+enrich_buildings_with_geojson_data (building_type_data, city)
+# for building in city.buildings:
+#     monthly_values = PeakLoads().peak_loads_from_hourly(building.lighting_electrical_demand[cte.HOUR])
+#     building.lighting_peak_load[cte.MONTH]=[x / cte.WATTS_HOUR_TO_JULES for x in monthly_values]
+#     building.lighting_peak_load[cte.YEAR] = [max(monthly_values) / cte.WATTS_HOUR_TO_JULES]
+#     monthly_values = PeakLoads().peak_loads_from_hourly(building.appliances_electrical_demand[cte.HOUR])
+#     building.appliances_electrical_demand[cte.MONTH]=[x / cte.WATTS_HOUR_TO_JULES for x in monthly_values]
+#     building.appliances_electrical_demand[cte.YEAR] = [max(monthly_values) / cte.WATTS_HOUR_TO_JULES]
+
+
+
+
+# def to_dict(building, total_floor_area):
+#     return {
+#         'roof_area': building.floor_area,
+#         'total_floor_area': total_floor_area,
+#         'year_of_construction' : building.year_of_construction,
+#         'type_function':building.function,
+#         'beam_kWh_per_m2': sum(building.beam[cte.HOUR])/ (3.6e6),
+#         'diffuse_kWh_per_m2': sum(building.diffuse[cte.HOUR])/ (3.6e6),
+#         'direct_normal_kWh_per_m2': sum(building.direct_normal[cte.HOUR])/ (3.6e6),
+#         'average_storey_height_meters': building.average_storey_height,
+#         'max_height_meters_meters': building.max_height,
+#         'global_horizontal_kWh_per_m2': sum(building.global_horizontal[cte.HOUR])/ (3.6e6),
+#         'appliances_peak_load_kW':building.appliances_peak_load[cte.YEAR][0]/ (1e3),
+#         'domestic_hot_water_peak_load_kW': building.domestic_hot_water_peak_load[cte.YEAR][0]/ (1e3),
+#         'heating_peak_load_kW': building.heating_peak_load[cte.YEAR][0]/ (1e3),
+#         'cooling_peak_load_kW': building.cooling_peak_load[cte.YEAR][0]/ (1e3),
+#         'lighting_peak_load_kW': building.lighting_peak_load[cte.YEAR][0]/ (1e3),
+#         'heating_demand_kWh_per_m2' : sum(building.heating_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
+#         'cooling_demand_kWh_per_m2' : sum(building.cooling_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
+#         'domestic_hot_water_heat_demand_kWh_per_m2': sum(building.domestic_hot_water_heat_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
+#         'appliances_electrical_demand_kWh_per_m2':sum(building.appliances_electrical_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
+#         'lighting_electrical_demand_kWh_per_m2': sum(building.lighting_electrical_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
+#         'heating_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.heating_demand[cte.HOUR]],
+#         'cooling_demand_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in  building.cooling_demand[cte.HOUR]],
+#         'domestic_hot_water_heat_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.domestic_hot_water_heat_demand[cte.HOUR]],
+#         'appliances_electrical_demand_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in  building.appliances_electrical_demand[cte.HOUR]],
+#         'lighting_electrical_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.lighting_electrical_demand[cte.HOUR]]
+#     }
+# buildings_dic={}
+
+
+for building in city.buildings:
+
+    total_floor_area = 0
+    for thermal_zone in building.thermal_zones_from_internal_zones:
+        total_floor_area += thermal_zone.total_floor_area
+        print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
+        building.energy_systems_archetype_name = 'system 1 gas'
+
+
+
+
+
+EnergySystemsFactory('montreal_custom', city).enrich()
+print('test')
+for building in city.buildings:
+  energy_systems = building.energy_systems
+  for energy_system in energy_systems:
+    generation_units = energy_system.generation_systems
+    if cte.HEATING in energy_system.demand_types:
+      for generation_unit in generation_units:
+        generation_unit.heat_efficiency = 0.96
+        
+
+# for building in city.buildings:
+#   print(building.heating_demand[cte.YEAR][0] / 3.6e6)
+#   print(building.name)
+#   total_floor_area = 0
+#   for thermal_zone in building.thermal_zones_from_internal_zones:
+#     total_floor_area += thermal_zone.total_floor_area
+#   print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
+
--- a/processor.py
+++ b/processor.py
@ -0,0 +1,345 @@
+import json
+from pathlib import Path
+import pandas as pd
+import geopandas as gpd
+
+
+#define output path
+output_path = (Path(__file__).parent / 'out_files').resolve()
+output_path.mkdir(parents=True, exist_ok=True)
+# Define output folders
+output_folders = ['building_type', 'building_type_2', 'building_type_3']
+#output paths containing energy+ results are read
+path1=r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\building_type\updated_buildings.geojson'
+path2=r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\building_type_2\updated_buildings.geojson'
+path3=r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\building_type_3\updated_buildings.geojson'
+with open(path1, 'r') as f:
+    building_type_data = json.load(f)
+with open(path2, 'r') as f:
+    building_type_data2 = json.load(f)
+with open(path3, 'r') as f:
+    building_type_data3 = json.load(f)    
+percentage_data = {
+    1646: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2672.550473, "total_floor_area": 26725.50473},
+    1647: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2653.626087, "total_floor_area": 26536.26087},
+    1648: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1056.787496, "total_floor_area": 10567.87496},
+    1649: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1906.620746, "total_floor_area": 19066.20746},
+    1650: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 659.1119416, "total_floor_area": 5272.895533},
+    1651: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1167.208109, "total_floor_area": 9337.664871},
+    1652: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1193.251653, "total_floor_area": 9546.013222},
+    1653: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1491.722543, "total_floor_area": 11933.78035},
+    1654: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1168.005028, "total_floor_area": 9344.040224},
+    1655: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1264.906961, "total_floor_area": 10119.25569},
+    1656: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1281.768818, "total_floor_area": 10254.15054},
+    1657: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 290.3886018, "total_floor_area": 2323.108814},
+    1658: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 847.5095193, "total_floor_area": 6780.076155},
+    1659: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1115.319153, "total_floor_area": 8922.553224},
+    1660: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 469.2918062, "total_floor_area": 3754.33445},
+    1661: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1292.298346, "total_floor_area": 10338.38677},
+    1662: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 625.7828863, "total_floor_area": 5006.263091},
+    1663: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1876.02897, "total_floor_area": 15008.23176},
+    1664: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1118.224781, "total_floor_area": 22364.49562},
+    1665: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1502.787808, "total_floor_area": 30055.75617},
+    1666: {"type1_%": 0.891045711, "type2_%": 0.108954289, "type3_%": 0, "roof_area": 3038.486076, "total_floor_area": 30384.86076},
+    1667: {"type1_%": 0.8, "type2_%": 0.2, "type3_%": 0, "roof_area": 1343.832818, "total_floor_area": 13438.32818},
+    1668: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 961.0996956, "total_floor_area": 4805.498478},
+    1669: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 489.1282111, "total_floor_area": 1956.512845},
+    1673: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 1693.141465, "total_floor_area": 5079.424396},
+    1674: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 3248.827576, "total_floor_area": 9746.482729},
+    1675: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 4086.842191, "total_floor_area": 12260.52657},
+    1676: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2786.114146, "total_floor_area": 11144.45658},
+    1677: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 5142.784184, "total_floor_area": 15428.35255},
+    1678: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 6068.664574, "total_floor_area": 18205.99372},
+    1679: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 5646.751407, "total_floor_area": 16940.25422},
+    1680: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 1601.765953, "total_floor_area": 4805.297859},
+    1681: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 9728.221797, "total_floor_area": 29184.66539},
+    1687: {"type1_%": 0.606611029, "type2_%": 0.28211422, "type3_%": 0.11127475, "roof_area": 4268.608743, "total_floor_area": 59760.52241},
+    1688: {"type1_%": 0.92, "type2_%": 0.08, "type3_%": 0, "roof_area": 2146.654828, "total_floor_area": 38639.7869},
+    1689: {"type1_%": 0.96, "type2_%": 0.04, "type3_%": 0, "roof_area": 2860.270711, "total_floor_area": 57205.41421},
+    1690: {"type1_%": 0.94, "type2_%": 0.06, "type3_%": 0, "roof_area": 2189.732519, "total_floor_area": 28466.52275},
+    1691: {"type1_%": 0.75, "type2_%": 0.25, "type3_%": 0, "roof_area": 3159.077523, "total_floor_area": 31590.77523},
+}
+
+
+# Define a function to calculate new demands based on percentages
+def calculate_demands(building_id, percentages, building_type_data, building_type_data2, building_type_data3):
+    new_demands = {}
+
+    for idx, feature in enumerate(building_type_data['features']):
+        if feature['properties']['id'] == str(building_id):
+            for demand_type in [
+                'heating_demand_kWh', 'cooling_demand_kWh',
+                'domestic_hot_water_heat_demand_kWh',
+                'appliances_electrical_demand_kWh',
+                'lighting_electrical_demand_kWh']:
+
+                demand_list_1 = building_type_data['features'][idx]['properties'].get(demand_type, [0])
+
+                # Initialize demand lists for type 2 and type 3 with zeros
+                demand_list_2 = [0] * len(demand_list_1)
+                demand_list_3 = [0] * len(demand_list_1)
+
+                # Update demand_list_2 if percentages['type2_%'] is not zero
+                if percentages['type2_%'] != 0:
+                    for feature2 in building_type_data2['features']:
+                        if feature2['properties']['id'] == str(building_id):
+                            demand_list_2 = feature2['properties'].get(demand_type, [0])
+                            break
+
+                # Update demand_list_3 if percentages['type3_%'] is not zero
+                if percentages['type3_%'] != 0:
+                    for feature3 in building_type_data3['features']:
+                        if feature3['properties']['id'] == str(building_id):
+                            demand_list_3 = feature3['properties'].get(demand_type, [0])
+                            break
+
+                # Ensure the demand lists are the same length by padding with zeros if necessary
+                max_len = max(len(demand_list_1), len(demand_list_2), len(demand_list_3))
+                demand_list_1 += [0] * (max_len - len(demand_list_1))
+                demand_list_2 += [0] * (max_len - len(demand_list_2))
+                demand_list_3 += [0] * (max_len - len(demand_list_3))
+
+                new_demand_list = [
+                    demand_list_1[i] * percentages['type1_%'] +
+                    demand_list_2[i] * percentages['type2_%'] +
+                    demand_list_3[i] * percentages['type3_%']
+                    for i in range(max_len)
+                ]
+                new_demands[demand_type] = new_demand_list
+
+    return new_demands
+
+
+# Calculate demand per square meter for each building
+def calculate_demand_per_m2(building_type_data, building_type_data2, building_type_data3, percentage_data):
+    results = {}
+
+    for building_id, percentages in percentage_data.items():
+        print(percentages ["total_floor_area"])
+        new_demands = calculate_demands(building_id, percentages, building_type_data, building_type_data2,
+                                        building_type_data3)
+
+        for idx, feature in enumerate(building_type_data['features']):
+            if feature['properties']['id'] == str(building_id):
+                total_floor_area = percentages ["total_floor_area"]
+
+                results[building_id] = {
+                    'roof_area':percentages["roof_area"],
+                    'total_floor_area': percentages["total_floor_area"],
+                    'heating_demand_kWh':new_demands.get('heating_demand_kWh', []),
+                    'cooling_demand_kWh':new_demands.get('cooling_demand_kWh', []),
+                    'domestic_hot_water_heat_demand_kWh': new_demands.get('domestic_hot_water_heat_demand_kWh', []),
+                    'appliances_electrical_demand_kWh': new_demands.get('appliances_electrical_demand_kWh', []),
+                    'lighting_electrical_demand_kWh': new_demands.get('lighting_electrical_demand_kWh', []),
+                    'heating_demand_kWh_per_m2': sum(new_demands.get('heating_demand_kWh', [])) / total_floor_area,
+                    'cooling_demand_kWh_per_m2': sum(new_demands.get('cooling_demand_kWh', [])) / total_floor_area,
+                    'domestic_hot_water_heat_demand_kWh_per_m2': sum(
+                        new_demands.get('domestic_hot_water_heat_demand_kWh', [])) / total_floor_area,
+                    'appliances_electrical_demand_kWh_per_m2': sum(
+                        new_demands.get('appliances_electrical_demand_kWh', [])) / total_floor_area,
+                    'lighting_electrical_demand_kWh_per_m2': sum(
+                        new_demands.get('lighting_electrical_demand_kWh', [])) / total_floor_area,
+                    'appliances_peak_load_kW': max(
+                        new_demands.get('appliances_electrical_demand_kWh', [])),
+                    'domestic_hot_water_peak_load_kW': max(
+                        new_demands.get('domestic_hot_water_heat_demand_kWh', [])) ,
+                    'heating_peak_load_kW': max(new_demands.get('heating_demand_kWh', [])),
+                    'cooling_peak_load_kW': max(new_demands.get('cooling_demand_kWh', [])),
+                    'lighting_peak_load_kW': max(
+                        new_demands.get('lighting_electrical_demand_kWh', [])),
+                }
+
+    return results
+
+# Example usage
+new_demands = calculate_demand_per_m2(building_type_data, building_type_data2, building_type_data3, percentage_data)
+# for building_id, demand_data in results.items():
+#     print(f"Building ID: {building_id}")
+#     for demand_type, value in demand_data.items():
+#         print(f"  {demand_type}: {value}")
+# new_demands = {}
+# for building_id, percentages in percentage_data.items():
+#     print(building_id)
+#     new_demands[building_id] = calculate_demands(building_id, percentages, building_type_data, building_type_data2, building_type_data3)
+
+# #
+import pandas as pd
+
+# Export the DataFrame to an Excel file
+output_path = r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\new_demands.xlsx'
+
+# Create an Excel writer object
+with pd.ExcelWriter(output_path, engine='xlsxwriter') as writer:
+    for building_id, demands in new_demands.items():
+        # Convert demands to a DataFrame
+        df_demands = pd.DataFrame(demands)
+        # Convert building_id to string and check its length
+        sheet_name = str(building_id)
+        if len(sheet_name) > 31:
+            sheet_name = sheet_name[:31]  # Truncate to 31 characters if necessary
+        # Write the DataFrame to a specific sheet named after the building_id
+        df_demands.to_excel(writer, sheet_name=sheet_name, index=False)
+
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Assume calculate_demand_per_m2 and results have been computed as before
+
+def extract_demands(data, building_id, demand_type):
+    for feature in data['features']:
+        if feature['properties']['id'] == str(building_id):
+            return feature['properties'].get(demand_type, [0])
+    return 0
+
+# Create a list of building IDs
+building_ids = list(percentage_data.keys())
+
+# Define the demand types to be plotted
+demand_types = [
+    'heating_demand_kWh_per_m2',
+    'cooling_demand_kWh_per_m2',
+    'domestic_hot_water_heat_demand_kWh_per_m2',
+    'appliances_electrical_demand_kWh_per_m2',
+    'lighting_electrical_demand_kWh_per_m2'
+]
+
+# Initialize lists to store values for plotting
+new_demands_values = {demand_type: [] for demand_type in demand_types}
+building_type_data_values = {demand_type: [] for demand_type in demand_types}
+building_type_data2_values = {demand_type: [] for demand_type in demand_types}
+building_type_data3_values = {demand_type: [] for demand_type in demand_types}
+
+# Populate the lists with the corresponding values
+for building_id in building_ids:
+    for demand_type in demand_types:
+        new_demands_values[demand_type].append(new_demands[building_id][demand_type])
+        building_type_data_values[demand_type].append(extract_demands(building_type_data, building_id, demand_type))
+        building_type_data2_values[demand_type].append(extract_demands(building_type_data2, building_id, demand_type))
+        building_type_data3_values[demand_type].append(extract_demands(building_type_data3, building_id, demand_type))
+
+# Plot the values for each demand type
+for demand_type in demand_types:
+    x = np.arange(len(building_ids))  # the label locations
+    width = 0.2  # the width of the bars
+
+    fig, ax = plt.subplots(figsize=(15, 7))
+
+    ax.bar(x - 1.5 * width, new_demands_values[demand_type], width, label='New Demands')
+    ax.bar(x - 0.5 * width, building_type_data_values[demand_type], width, label='Old Demands - Type 1')
+    ax.bar(x + 0.5 * width, building_type_data2_values[demand_type], width, label='Old Demands - Type 2')
+    ax.bar(x + 1.5 * width, building_type_data3_values[demand_type], width, label='Old Demands - Type 3')
+
+    # Add some text for labels, title, and custom x-axis tick labels, etc.
+    ax.set_xlabel('Building ID')
+    ax.set_ylabel(f'{demand_type} (kWh/m²)')
+    ax.set_title(f'Comparison of {demand_type} by Building ID')
+    ax.set_xticks(x)
+    ax.set_xticklabels(building_ids, rotation=90)
+    ax.legend()
+
+    fig.tight_layout()
+
+    plt.show()
+
+
+
+
+# Load the existing GeoJSON file
+geojson_file_path =r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\building_type\updated_buildings.geojson' # Replace with the actual path
+with open(geojson_file_path, 'r') as f:
+    geojson_data = json.load(f)
+
+# Define output path
+output_path = (Path(__file__).parent / 'out_files').resolve()
+output_path.mkdir(parents=True, exist_ok=True)
+
+# Load additional data from the CSV files
+solar_radiation_tilted_path = r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\solar_radiation_tilted_selected_buildings.csv'
+solar_radiation_horizontal_path = r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\solar_radiation_horizontal_selected_buildings.csv'
+
+
+import csv
+
+def load_solar_data(file_path, key_name):
+    solar_data = {}
+    with open(file_path, 'r') as f:
+        reader = csv.reader(f)
+        headers = next(reader)[1:]  # Skip the first header and get building IDs
+        
+        for row in reader:
+            for building_id, solar_value in zip(headers, row[1:]):  # Skip the first column in each row
+                building_id = int(building_id)
+                solar_value = float(solar_value)
+                if building_id not in solar_data:
+                    solar_data[building_id] = {}
+                if key_name not in solar_data[building_id]:
+                    solar_data[building_id][key_name] = []
+                solar_data[building_id][key_name].append(solar_value)
+    return solar_data
+
+
+solar_radiation_tilted = load_solar_data(solar_radiation_tilted_path, 'solar_radiation_tilted')
+solar_radiation_horizontal = load_solar_data(solar_radiation_horizontal_path, 'solar_radiation_horizontal')
+
+# Merge the solar data into a single dictionary
+solar_data = {}
+for building_id in set(solar_radiation_tilted.keys()).union(solar_radiation_horizontal.keys()):
+    solar_data[building_id] = {}
+    if building_id in solar_radiation_tilted:
+        solar_data[building_id]['solar_radiation_tilted'] = solar_radiation_tilted[building_id][
+            'solar_radiation_tilted']
+    if building_id in solar_radiation_horizontal:
+        solar_data[building_id]['solar_radiation_horizontal'] = solar_radiation_horizontal[building_id][
+            'solar_radiation_horizontal']
+
+# Example data for results (use actual results data in practice)
+results = {
+    1646: {
+        "appliances_peak_load_kW": 269.020198862769,
+        "domestic_hot_water_peak_load_kW": 1477.6420222203596,
+        "heating_peak_load_kW": 1650.4097261870008,
+        "cooling_peak_load_kW": 1441.1027982978985,
+        "lighting_peak_load_kW": 295.92221874904584,
+        "heating_demand_kWh_per_m2": 40.4922413088911,
+        "cooling_demand_kWh_per_m2": 20.33948801154316,
+        "domestic_hot_water_heat_demand_kWh_per_m2": 60.25215210051923,
+        "appliances_electrical_demand_kWh_per_m2": 19.352038723954898,
+        "lighting_electrical_demand_kWh_per_m2": 11.246090428260413,
+    }
+    # Add similar dictionaries for other building IDs
+}
+
+# Update GeoJSON with percentage data, results, and solar data
+for feature in geojson_data['features']:
+    building_id = int(feature['properties']['id'])
+
+    # Update with percentage data
+    if building_id in percentage_data:
+        for key, value in percentage_data[building_id].items():
+            feature['properties'][key] = value
+
+    # Update with results data
+    if building_id in new_demands:
+        for key, value in new_demands[building_id].items():
+            feature['properties'][key] = value
+
+    # Update with solar data
+    if building_id in solar_data:
+        for key, value in solar_data[building_id].items():
+            feature['properties'][key] = value
+        # Calculate and add the sums
+        feature['properties']['sum_solar_radiation_tilted_kWh_per_m2'] = sum(solar_data[building_id]['solar_radiation_tilted']) / 3.6e6
+        feature['properties']['sum_solar_radiation_horizontal_kWh_per_m2'] = sum(solar_data[building_id]['solar_radiation_horizontal']) / 3.6e6
+        # Add 8760 arrays divided by 3.6e6
+        feature['properties']['solar_radiation_tilted_kWh_per_m2'] = [value / 3.6e6 for value in solar_data[building_id]['solar_radiation_tilted']]
+        feature['properties']['solar_radiation_horizontal_kWh_per_m2'] = [value / 3.6e6 for value in solar_data[building_id]['solar_radiation_horizontal']]
+
+# Save the updated GeoJSON data to a new file
+updated_geojson_file_path = output_path / 'updated_buildings_with_all_data.geojson'
+with open(updated_geojson_file_path, 'w') as f:
+    json.dump(geojson_data, f)
+
+print(f"Updated GeoJSON data saved to {updated_geojson_file_path}")
+
+
--- a/updated_buildings.geojson
+++ b/updated_buildings.geojson
@ -0,0 +1,113 @@
+{
+    "project": {
+        "country": "-",
+        "city": "Montreal, QC",
+        "latitude": 45.5019,
+        "cad_coordinates": [
+            0.0,
+            0.0
+        ],
+        "longitude": -73.5674,
+        "time_zone": -5.0,
+        "elevation": 40.0
+    },
+    "type": "FeatureCollection",
+    "mappers": [],
+    "features": [
+        {
+            "geometry": {
+                "type": "Polygon",
+                "coordinates": [
+                    [
+                        [
+                            -73.56460598928051,
+                            45.49740281507067
+                        ],
+                        [
+                            -73.56460599751905,
+                            45.497401799005075
+                        ],
+                        [
+                            -73.56517531829724,
+                            45.49740281063829
+                        ],
+                        [
+                            -73.5651753100587,
+                            45.49734347035089
+                        ],
+                        [
+                            -73.56517457279315,
+                            45.49734347035089
+                        ],
+                        [
+                            -73.56517457279315,
+                            45.497253770511335
+                        ],
+                        [
+                            -73.5651753100587,
+                            45.497253770511335
+                        ],
+                        [
+                            -73.56537248633211,
+                            45.497253758929254
+                        ],
+                        [
+                            -73.56537191925358,
+                            45.497208766225164
+                        ],
+                        [
+                            -73.56537192028448,
+                            45.497015574816565
+                        ],
+                        [
+                            -73.56455529412895,
+                            45.49701527683656
+                        ],
+                        [
+                            -73.56455529412895,
+                            45.49725375536205
+                        ],
+                        [
+                            -73.56460598928051,
+                            45.497253770511335
+                        ],
+                        [
+                            -73.56460598928051,
+                            45.49740281507067
+                        ]
+                    ]
+                ]
+            },
+            "type": "Feature",
+            "id": "1691",
+            "properties": {
+                "footprint_area": 2416.2204562020706,
+                "floor_height": 3.9999999999999996,
+                "year_built": 2024,
+                "detailed_model_filename": "C:\\Users\\umroot\\Downloads\\Group 3\\hb_json\\Building_1691_e4a64903.json",
+                "type": "Building",
+                "building_type": "1000",
+                "number_of_stories": 8,
+                "name": "Building_1691",
+                "number_of_stories_above_ground": 8,
+                "floor_area": 19329.763649616565,
+                "maximum_roof_height": 31.999999999999996,
+                "year_of_construction": 2024,
+                "type_function": "residential",
+                "heating_demand": [
+                    4528783398296.355
+                ],
+                "cooling_demand": [
+                    2074220778983.8276
+                ],
+                "domestic_hot_water_heat_demand": [
+                    6848139964182.653
+                ],
+                "appliances_electrical_demand": [
+                    2199514382726.829
+                ],
+                "lighting_electrical_demand": [
+                    1278208358263.9053
+                ],
+                "walls": [
+
Author	SHA1	Message	Date
Andrea Gabaldon Moreno	0ce76bef13	20240722 changes	2024-07-22 18:14:54 -04:00
Andrea Gabaldon Moreno	76a6f5f930	2024-07-17_course_update Lachine data model is assessed for several building types and U-values	2024-07-17 18:39:52 -04:00