20240801
This commit is contained in:
Andrea Gabaldon Moreno
parent
1488c9dbce
commit
28c88e2978
@ -198,7 +198,7 @@
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<equipments>
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<equipments>
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<generation_id>3</generation_id>
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<generation_id>3</generation_id>
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<distribution_id>8</distribution_id>
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<distribution_id>8</distribution_id>
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g </equipments>
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</equipments>
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</system>
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</system>
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<system id="5">
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<system id="5">
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<name>Single zone packaged rooftop unit with electrical resistance furnace and baseboards and fuel boiler for acs</name>
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<name>Single zone packaged rooftop unit with electrical resistance furnace and baseboards and fuel boiler for acs</name>
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223
main.py
223
main.py
@ -1,5 +1,6 @@
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from pathlib import Path
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from pathlib import Path
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from scripts.ep_workflow import energy_plus_workflow
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from scripts.ep_workflow import energy_plus_workflow
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from hub.helpers.monthly_values import MonthlyValues
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from hub.imports.geometry_factory import GeometryFactory
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from hub.imports.geometry_factory import GeometryFactory
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from hub.helpers.dictionaries import Dictionaries
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from hub.helpers.dictionaries import Dictionaries
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from hub.imports.construction_factory import ConstructionFactory
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from hub.imports.construction_factory import ConstructionFactory
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@ -7,36 +8,24 @@ from hub.imports.usage_factory import UsageFactory
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from hub.imports.weather_factory import WeatherFactory
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from hub.imports.weather_factory import WeatherFactory
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import hub.helpers.constants as cte
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import hub.helpers.constants as cte
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from hub.imports.energy_systems_factory import EnergySystemsFactory
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from hub.imports.energy_systems_factory import EnergySystemsFactory
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from hub.helpers.peak_loads import PeakLoads
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import geopandas as gpd
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import geopandas as gpd
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import json
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# Specify the GeoJSON file path
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# Specify the GeoJSON file path
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input_files_path = (Path(__file__).parent / 'input_files')
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input_files_path = (Path(__file__).parent / 'input_files')
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building_type_2_modelling=2
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#'Lachine_New_Developments.geojson'
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geojson_file_path = input_files_path / 'Lachine_moved_2024_type.geojson'
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if building_type_2_modelling==1:
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gdf = gpd.read_file(geojson_file_path)
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# Filter gdf when 'building_type_2' is not null
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filtered_gdf = gdf[gdf['building_type_2'].notnull()]
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output_geojson =input_files_path /'Lachine_moved_2024_type_2.geojson'
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geojson_file_path =output_geojson
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filtered_gdf.to_file(output_geojson, driver='GeoJSON')
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print(f"New GeoJSON saved in: {output_geojson}")
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if building_type_2_modelling==2:
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gdf = gpd.read_file(geojson_file_path)
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# Filter gdf when 'building_type_3' is not null
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filtered_gdf = gdf[gdf['building_type_3'].notnull()]
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output_geojson =input_files_path /'Lachine_moved_2024_type_3.geojson'
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geojson_file_path = output_geojson
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filtered_gdf.to_file(output_geojson, driver='GeoJSON')
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print(f"New GeoJSON saved in: {output_geojson}")
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output_path = (Path(__file__).parent / 'out_files').resolve()
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output_path = (Path(__file__).parent / 'out_files').resolve()
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output_path.mkdir(parents=True, exist_ok=True)
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output_path.mkdir(parents=True, exist_ok=True)
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geojson_file_path_baseline = output_path / 'updated_buildings_with_all_data_baseline.geojson'
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geojson_file_path_2024 = output_path / 'updated_buildings_with_all_data.geojson'
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with open(geojson_file_path_baseline , 'r') as f:
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building_type_data = json.load(f)
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with open(geojson_file_path_2024, 'r') as f:
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building_type_data_2024 = json.load(f)
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# Create city object from GeoJSON file
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# Create city object from GeoJSON file
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city = GeometryFactory('geojson',
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city = GeometryFactory('geojson',
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path=geojson_file_path,
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path=geojson_file_path_baseline,
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height_field='maximum_roof_height',
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height_field='maximum_roof_height',
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year_of_construction_field='year_built',
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year_of_construction_field='year_built',
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function_field='building_type',
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function_field='building_type',
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@ -45,34 +34,97 @@ city = GeometryFactory('geojson',
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ConstructionFactory('nrcan', city).enrich()
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ConstructionFactory('nrcan', city).enrich()
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UsageFactory('nrcan', city).enrich()
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UsageFactory('nrcan', city).enrich()
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WeatherFactory('epw', city).enrich()
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WeatherFactory('epw', city).enrich()
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energy_plus_workflow(city)
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# #energy plus is not going to be processed here, as demand has been obtained before
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# energy_plus_workflow(city)
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#SRA algorithm
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percentage_data = {
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1646: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2672.550473, "total_floor_area": 26725.50473},
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1647: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2653.626087, "total_floor_area": 26536.26087},
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1648: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1056.787496, "total_floor_area": 10567.87496},
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1649: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1906.620746, "total_floor_area": 19066.20746},
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1650: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 659.1119416, "total_floor_area": 5272.895533},
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1651: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1167.208109, "total_floor_area": 9337.664871},
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1652: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1193.251653, "total_floor_area": 9546.013222},
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1653: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1491.722543, "total_floor_area": 11933.78035},
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1654: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1168.005028, "total_floor_area": 9344.040224},
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1655: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1264.906961, "total_floor_area": 10119.25569},
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1656: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1281.768818, "total_floor_area": 10254.15054},
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1657: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 290.3886018, "total_floor_area": 2323.108814},
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1658: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 847.5095193, "total_floor_area": 6780.076155},
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1659: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1115.319153, "total_floor_area": 8922.553224},
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1660: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 469.2918062, "total_floor_area": 3754.33445},
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1661: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1292.298346, "total_floor_area": 10338.38677},
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1662: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 625.7828863, "total_floor_area": 5006.263091},
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1663: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1876.02897, "total_floor_area": 15008.23176},
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1664: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1118.224781, "total_floor_area": 22364.49562},
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1665: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1502.787808, "total_floor_area": 30055.75617},
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1666: {"type1_%": 0.891045711, "type2_%": 0.108954289, "type3_%": 0, "roof_area": 3038.486076, "total_floor_area": 30384.86076},
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1667: {"type1_%": 0.8, "type2_%": 0.2, "type3_%": 0, "roof_area": 1343.832818, "total_floor_area": 13438.32818},
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1668: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 961.0996956, "total_floor_area": 4805.498478},
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1669: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 489.1282111, "total_floor_area": 1956.512845},
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1673: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 1693.141465, "total_floor_area": 5079.424396},
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1674: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 3248.827576, "total_floor_area": 9746.482729},
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1675: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 4086.842191, "total_floor_area": 12260.52657},
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1676: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2786.114146, "total_floor_area": 11144.45658},
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1677: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 5142.784184, "total_floor_area": 15428.35255},
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1678: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 6068.664574, "total_floor_area": 18205.99372},
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1679: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 5646.751407, "total_floor_area": 16940.25422},
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1680: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 1601.765953, "total_floor_area": 4805.297859},
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1681: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 9728.221797, "total_floor_area": 29184.66539},
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1687: {"type1_%": 0.606611029, "type2_%": 0.28211422, "type3_%": 0.11127475, "roof_area": 4268.608743, "total_floor_area": 59760.52241},
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1688: {"type1_%": 0.92, "type2_%": 0.08, "type3_%": 0, "roof_area": 2146.654828, "total_floor_area": 38639.7869},
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1689: {"type1_%": 0.96, "type2_%": 0.04, "type3_%": 0, "roof_area": 2860.270711, "total_floor_area": 57205.41421},
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1690: {"type1_%": 0.94, "type2_%": 0.06, "type3_%": 0, "roof_area": 2189.732519, "total_floor_area": 28466.52275},
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1691: {"type1_%": 0.75, "type2_%": 0.25, "type3_%": 0, "roof_area": 3159.077523, "total_floor_area": 31590.77523},
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}
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def enrich_buildings_with_geojson_data (building_type_data, city):
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for building in city.buildings:
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for idx, feature in enumerate(building_type_data['features']):
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if feature['properties']['id'] == str(building.name):
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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])]
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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])]
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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])]
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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])]
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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])]
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building.heating_demand[cte.MONTH] = MonthlyValues.get_total_month(building.heating_demand[cte.HOUR])
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building.cooling_demand[cte.MONTH] = MonthlyValues.get_total_month(building.cooling_demand[cte.HOUR])
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building.domestic_hot_water_heat_demand[cte.MONTH] = (MonthlyValues.get_total_month(building.domestic_hot_water_heat_demand[cte.HOUR]))
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building.appliances_electrical_demand[cte.MONTH] = (MonthlyValues.get_total_month(building.appliances_electrical_demand[cte.HOUR]))
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building.lighting_electrical_demand[cte.MONTH] = (MonthlyValues.get_total_month(building.lighting_electrical_demand[cte.HOUR]))
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building.heating_demand[cte.YEAR] = [sum(building.heating_demand[cte.MONTH])]
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building.cooling_demand[cte.YEAR] = [sum(building.cooling_demand[cte.MONTH])]
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building.domestic_hot_water_heat_demand[cte.YEAR] = [sum(building.domestic_hot_water_heat_demand[cte.MONTH])]
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building.appliances_electrical_demand[cte.YEAR] = [sum(building.appliances_electrical_demand[cte.MONTH])]
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building.lighting_electrical_demand[cte.YEAR] = [sum(building.lighting_electrical_demand[cte.MONTH])]
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enrich_buildings_with_geojson_data (building_type_data, city)
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print('test')
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for building in city.buildings:
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building.energy_systems_archetype_name = 'system 1 electricity pv'
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EnergySystemsFactory('montreal_custom', city).enrich()
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# for building in city.buildings:
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# energy_systems = building.energy_systems
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# for energy_system in energy_systems:
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# generation_units = energy_system.generation_systems
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# if cte.HEATING in energy_system.demand_types:
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# for generation_unit in generation_units:
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# generation_unit.heat_efficiency = 0.96
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def to_dict(building, total_floor_area):
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def to_dict(building, total_floor_area):
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return {
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return {
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'roof_area': building.floor_area,
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'roof_area': building.floor_area,
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'total_floor_area': total_floor_area,
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'total_floor_area': total_floor_area,
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'year_of_construction' : building.year_of_construction,
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'heating_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.energy_consumption_breakdown[cte.HOUR]],
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'type_function':building.function,
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'cooling_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.cooling_demand[cte.HOUR]],
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'beam_kWh_per_m2': sum(building.beam[cte.HOUR])/ (3.6e6),
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'domestic_hot_water_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.domestic_hot_water_heat_demand[cte.HOUR]],
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'diffuse_kWh_per_m2': sum(building.diffuse[cte.HOUR])/ (3.6e6),
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'appliances_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.appliances_electrical_demand[cte.HOUR]],
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'direct_normal_kWh_per_m2': sum(building.direct_normal[cte.HOUR])/ (3.6e6),
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'lighting_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.lighting_electrical_demand[cte.HOUR]]
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'average_storey_height_meters': building.average_storey_height,
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'max_height_meters_meters': building.max_height,
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'global_horizontal_kWh_per_m2': sum(building.global_horizontal[cte.HOUR])/ (3.6e6),
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'appliances_peak_load_kW':building.appliances_peak_load[cte.YEAR][0]/ (1e3),
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'domestic_hot_water_peak_load_kW': building.domestic_hot_water_peak_load[cte.YEAR][0]/ (1e3),
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'heating_peak_load_kW': building.heating_peak_load[cte.YEAR][0]/ (1e3),
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'cooling_peak_load_kW': building.cooling_peak_load[cte.YEAR][0]/ (1e3),
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'lighting_peak_load_kW': building.lighting_peak_load[cte.YEAR][0]/ (1e3),
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'heating_demand_kWh_per_m2' : sum(building.heating_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
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'cooling_demand_kWh_per_m2' : sum(building.cooling_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
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'domestic_hot_water_heat_demand_kWh_per_m2': sum(building.domestic_hot_water_heat_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
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'appliances_electrical_demand_kWh_per_m2':sum(building.appliances_electrical_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
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'lighting_electrical_demand_kWh_per_m2': sum(building.lighting_electrical_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
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'heating_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.heating_demand[cte.HOUR]],
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'cooling_demand_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.cooling_demand[cte.HOUR]],
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'domestic_hot_water_heat_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.domestic_hot_water_heat_demand[cte.HOUR]],
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'appliances_electrical_demand_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.appliances_electrical_demand[cte.HOUR]],
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'lighting_electrical_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.lighting_electrical_demand[cte.HOUR]]
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}
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}
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buildings_dic={}
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buildings_dic={}
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@ -82,78 +134,15 @@ for building in city.buildings:
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total_floor_area = 0
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total_floor_area = 0
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for thermal_zone in building.thermal_zones_from_internal_zones:
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for thermal_zone in building.thermal_zones_from_internal_zones:
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total_floor_area += thermal_zone.total_floor_area
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total_floor_area += thermal_zone.total_floor_area
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print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
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building.energy_systems_archetype_name = 'system 1 gas'
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buildings_dic[building.name] = to_dict(building,total_floor_area)
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print('test')
|
|
||||||
|
|
||||||
""""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:
|
|
||||||
# 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:
|
# for building in city.buildings:
|
||||||
# print(building.name)
|
# print(building.heating_demand[cte.YEAR][0] / 3.6e6)
|
||||||
# print(building.year_of_construction)
|
# print(building.name)
|
||||||
# print(building.usages_percentage)
|
# 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))
|
||||||
|
|
||||||
|
|||||||
@ -1,155 +0,0 @@
|
|||||||
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_baseline = output_path / 'updated_buildings_with_all_data_baseline.geojson'
|
|
||||||
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)
|
|
||||||
with open(geojson_file_path_baseline, 'r') as f:
|
|
||||||
building_type_data_baseline = 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]
|
|
||||||
|
|
||||||
|
|
||||||
print('test')
|
|
||||||
for building in city.buildings:
|
|
||||||
building.energy_systems_archetype_name = 'system 1 gas'
|
|
||||||
|
|
||||||
|
|
||||||
EnergySystemsFactory('montreal_custom', city).enrich()
|
|
||||||
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
|
|
||||||
def to_dict(building, total_floor_area):
|
|
||||||
return {
|
|
||||||
'roof_area': building.floor_area,
|
|
||||||
'total_floor_area': total_floor_area,
|
|
||||||
'heating_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.energy_consumption_breakdown[cte.HOUR]],
|
|
||||||
# 'cooling_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.cooling_demand[cte.HOUR]],
|
|
||||||
# 'domestic_hot_water_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.domestic_hot_water_heat_demand[cte.HOUR]],
|
|
||||||
# 'appliances_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.appliances_electrical_demand[cte.HOUR]],
|
|
||||||
# 'lighting_consumption_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))
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
# 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))
|
|
||||||
|
|
||||||
159
old_main.py
Normal file
159
old_main.py
Normal file
@ -0,0 +1,159 @@
|
|||||||
|
from pathlib import Path
|
||||||
|
from scripts.ep_workflow import energy_plus_workflow
|
||||||
|
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
|
||||||
|
import geopandas as gpd
|
||||||
|
# Specify the GeoJSON file path
|
||||||
|
input_files_path = (Path(__file__).parent / 'input_files')
|
||||||
|
|
||||||
|
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
|
||||||
|
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_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:
|
||||||
|
|
||||||
|
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')
|
||||||
|
|
||||||
|
""""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:
|
||||||
|
# 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.name)
|
||||||
|
# print(building.year_of_construction)
|
||||||
|
# print(building.usages_percentage)
|
||||||
Loading…
Reference in New Issue
Block a user