2024-07-17_course_update
Lachine data model is assessed for several building types and U-values
This commit is contained in:
Andrea Gabaldon Moreno
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73a7a12a97
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"""
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Dictionaries module for hub function to nrcan construction function
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SPDX - License - Identifier: LGPL - 3.0 - or -later
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Copyright © 2022 Concordia CERC group
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Project Coder Guille Gutierrez Guillermo.GutierrezMorote@concordia.ca
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"""
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import hub.helpers.constants as cte
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class HubFunctionToNrcanConstructionFunction:
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"""
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Hub function to nrcan construction function class
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"""
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def __init__(self):
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self._dictionary = {
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cte.RESIDENTIAL: 'MidriseApartment',
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cte.SINGLE_FAMILY_HOUSE: 'MidriseApartment',
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cte.MULTI_FAMILY_HOUSE: 'HighriseApartment',
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cte.ROW_HOUSE: 'MidriseApartment',
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cte.MID_RISE_APARTMENT: 'MidriseApartment',
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cte.HIGH_RISE_APARTMENT: 'HighriseApartment',
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cte.OFFICE_AND_ADMINISTRATION: 'MediumOffice',
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cte.SMALL_OFFICE: 'SmallOffice',
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cte.MEDIUM_OFFICE: 'MediumOffice',
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cte.LARGE_OFFICE: 'LargeOffice',
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cte.COURTHOUSE: 'MediumOffice',
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cte.FIRE_STATION: 'n/a',
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cte.PENITENTIARY: 'LargeHotel',
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cte.POLICE_STATION: 'n/a',
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cte.POST_OFFICE: 'MediumOffice',
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cte.LIBRARY: 'MediumOffice',
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cte.EDUCATION: 'SecondarySchool',
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cte.PRIMARY_SCHOOL: 'PrimarySchool',
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cte.PRIMARY_SCHOOL_WITH_SHOWER: 'PrimarySchool',
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cte.SECONDARY_SCHOOL: 'SecondarySchool',
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cte.UNIVERSITY: 'SecondarySchool',
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cte.LABORATORY_AND_RESEARCH_CENTER: 'SecondarySchool',
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cte.STAND_ALONE_RETAIL: 'RetailStandalone',
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cte.HOSPITAL: 'Hospital',
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cte.OUT_PATIENT_HEALTH_CARE: 'Outpatient',
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cte.HEALTH_CARE: 'Outpatient',
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cte.RETIREMENT_HOME_OR_ORPHANAGE: 'SmallHotel',
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cte.COMMERCIAL: 'RetailStripmall',
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cte.STRIP_MALL: 'RetailStripmall',
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cte.SUPERMARKET: 'RetailStripmall',
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cte.RETAIL_SHOP_WITHOUT_REFRIGERATED_FOOD: 'RetailStandalone',
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cte.RETAIL_SHOP_WITH_REFRIGERATED_FOOD: 'RetailStandalone',
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cte.RESTAURANT: 'FullServiceRestaurant',
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cte.QUICK_SERVICE_RESTAURANT: 'QuickServiceRestaurant',
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cte.FULL_SERVICE_RESTAURANT: 'FullServiceRestaurant',
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cte.HOTEL: 'SmallHotel',
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cte.HOTEL_MEDIUM_CLASS: 'SmallHotel',
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cte.SMALL_HOTEL: 'SmallHotel',
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cte.LARGE_HOTEL: 'LargeHotel',
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cte.DORMITORY: 'SmallHotel',
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cte.EVENT_LOCATION: 'n/a',
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cte.CONVENTION_CENTER: 'n/a',
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cte.HALL: 'n/a',
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cte.GREEN_HOUSE: 'n/a',
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cte.INDUSTRY: 'n/a',
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cte.WORKSHOP: 'n/a',
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cte.WAREHOUSE: 'Warehouse',
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cte.WAREHOUSE_REFRIGERATED: 'Warehouse',
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cte.SPORTS_LOCATION: 'n/a',
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cte.SPORTS_ARENA: 'n/a',
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cte.GYMNASIUM: 'n/a',
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cte.MOTION_PICTURE_THEATRE: 'n/a',
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cte.MUSEUM: 'n/a',
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cte.PERFORMING_ARTS_THEATRE: 'n/a',
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cte.TRANSPORTATION: 'n/a',
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cte.AUTOMOTIVE_FACILITY: 'n/a',
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cte.PARKING_GARAGE: 'n/a',
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cte.RELIGIOUS: 'n/a',
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cte.NON_HEATED: 'n/a',
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cte.DATACENTER: 'n/a',
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cte.FARM: 'n/a'
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}
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@property
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def dictionary(self) -> dict:
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"""
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Get the dictionary
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:return: {}
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"""
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return self._dictionary
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@ -1,105 +0,0 @@
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"""
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NrcanPhysicsParameters import the construction and material information defined by NRCAN
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SPDX - License - Identifier: LGPL - 3.0 - or -later
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Copyright © 2022 Concordia CERC group
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Project Coder Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
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"""
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import logging
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from hub.catalog_factories.construction_catalog_factory import ConstructionCatalogFactory
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from hub.city_model_structure.building_demand.thermal_archetype import ThermalArchetype
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from hub.city_model_structure.building_demand.construction import Construction
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from hub.city_model_structure.building_demand.layer import Layer
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from hub.helpers.dictionaries import Dictionaries
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from hub.imports.construction.helpers.construction_helper import ConstructionHelper
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class NrcanPhysicsParameters:
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"""
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NrcanPhysicsParameters class
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"""
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def __init__(self, city, divide_in_storeys=False):
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self._city = city
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self._divide_in_storeys = divide_in_storeys
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self._climate_zone = ConstructionHelper.city_to_nrcan_climate_zone(city.climate_reference_city)
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def enrich_buildings(self):
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"""
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Returns the city with the construction parameters assigned to the buildings
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"""
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city = self._city
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nrcan_catalog = ConstructionCatalogFactory('nrcan').catalog
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for building in city.buildings:
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if building.function not in Dictionaries().hub_function_to_nrcan_construction_function:
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logging.error('Building %s has an unknown building function %s', building.name, building.function)
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continue
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function = Dictionaries().hub_function_to_nrcan_construction_function[building.function]
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try:
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archetype = self._search_archetype(nrcan_catalog, function, building.year_of_construction, self._climate_zone)
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except KeyError:
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logging.error('Building %s has unknown construction archetype for building function: %s '
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'[%s], building year of construction: %s and climate zone %s', building.name, function,
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building.function, building.year_of_construction, self._climate_zone)
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continue
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thermal_archetype = ThermalArchetype()
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self._assign_values(thermal_archetype, archetype)
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for internal_zone in building.internal_zones:
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internal_zone.thermal_archetype = thermal_archetype
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@staticmethod
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def _search_archetype(nrcan_catalog, function, year_of_construction, climate_zone):
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nrcan_archetypes = nrcan_catalog.entries('archetypes')
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for building_archetype in nrcan_archetypes:
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construction_period_limits = building_archetype.construction_period.split('_')
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if int(construction_period_limits[0]) <= int(year_of_construction) <= int(construction_period_limits[1]):
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if str(function) == str(building_archetype.function) and climate_zone == str(building_archetype.climate_zone):
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return building_archetype
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raise KeyError('archetype not found')
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@staticmethod
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def _assign_values(thermal_archetype, catalog_archetype):
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thermal_archetype.average_storey_height = catalog_archetype.average_storey_height
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thermal_archetype.extra_loses_due_to_thermal_bridges = catalog_archetype.extra_loses_due_to_thermal_bridges
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thermal_archetype.thermal_capacity = catalog_archetype.thermal_capacity
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thermal_archetype.indirect_heated_ratio = 0
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thermal_archetype.infiltration_rate_for_ventilation_system_on = catalog_archetype.infiltration_rate_for_ventilation_system_on
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thermal_archetype.infiltration_rate_for_ventilation_system_off = catalog_archetype.infiltration_rate_for_ventilation_system_off
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_constructions = []
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for catalog_construction in catalog_archetype.constructions:
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construction = Construction()
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construction.type = catalog_construction.type
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construction.name = catalog_construction.name
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if catalog_construction.window_ratio is not None:
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for _orientation in catalog_construction.window_ratio:
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if catalog_construction.window_ratio[_orientation] is None:
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catalog_construction.window_ratio[_orientation] = 0
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construction.window_ratio = catalog_construction.window_ratio
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_layers = []
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for layer_archetype in catalog_construction.layers:
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layer = Layer()
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layer.thickness = layer_archetype.thickness
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archetype_material = layer_archetype.material
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layer.material_name = archetype_material.name
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layer.no_mass = archetype_material.no_mass
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if archetype_material.no_mass:
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layer.thermal_resistance = archetype_material.thermal_resistance
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else:
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layer.density = archetype_material.density
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layer.conductivity = archetype_material.conductivity
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layer.specific_heat = archetype_material.specific_heat
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layer.solar_absorptance = archetype_material.solar_absorptance
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layer.thermal_absorptance = archetype_material.thermal_absorptance
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layer.visible_absorptance = archetype_material.visible_absorptance
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_layers.append(layer)
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construction.layers = _layers
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if catalog_construction.window is not None:
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window_archetype = catalog_construction.window
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construction.window_frame_ratio = window_archetype.frame_ratio
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construction.window_g_value = window_archetype.g_value
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construction.window_overall_u_value = window_archetype.overall_u_value
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_constructions.append(construction)
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thermal_archetype.constructions = _constructions
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building_aliases = []
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building_aliases = []
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if 'id' in feature:
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if 'id' in feature:
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building_name = feature['id']
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building_name = feature['id']
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elif 'id' in feature['properties']:
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building_name = feature['properties']['id']
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else:
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else:
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building_name = uuid.uuid4()
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building_name = uuid.uuid4()
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if self._aliases_field is not None:
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if self._aliases_field is not None:
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45
input_files/Lachine_moved_existing_year_type.geojson
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input_files/Lachine_moved_existing_year_type.geojson
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{
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"type": "FeatureCollection",
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"name": "Lachine_moved_existing_year_type",
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"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
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"features": [
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{ "type": "Feature", "properties": { "id": "1673", "footprint_area": 1294.9983604130321, "floor_height": 4.0, "year_built": 1990, "detailed_model_filename": "C:\\Users\\umroot\\Downloads\\Group 3\\hb_json\\Building_1673_bf6023ad.json", "type": "Building", "building_type": "6000", "number_of_stories": 3, "name": "Building_1673", "number_of_stories_above_ground": 3, "floor_area": 3884.9950812390962, "maximum_roof_height": 12.0, "building_type_2": 6111, "building_type_3": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -73.667372756629021, 45.435676695791244 ], [ -73.667372756629021, 45.435369020278436 ], [ -73.666888584086863, 45.435363806039966 ], [ -73.666884389888097, 45.435671430856793 ], [ -73.667372756629021, 45.435676695791244 ] ] ] } },
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{ "type": "Feature", "properties": { "id": "1646", "footprint_area": 2044.068283611894, "floor_height": 3.9999999999999996, "year_built": 2019, "detailed_model_filename": "C:\\Users\\umroot\\Downloads\\Group 3\\hb_json\\Building_1646_18838a5f.json", "type": "Building", "building_type": "1000", "number_of_stories": 8, "name": "Building_1646", "number_of_stories_above_ground": 8, "floor_area": 16352.546268895137, "maximum_roof_height": 31.999999999999996, "building_type_2": null, "building_type_3": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -73.66455280450829, 45.435744913502532 ], [ -73.664552546221131, 45.435924884126429 ], [ -73.665143168622009, 45.435925291049998 ], [ -73.665143472602168, 45.435897888505529 ], [ -73.665271574391269, 45.435897888505529 ], [ -73.665271574391269, 45.43592397028798 ], [ -73.665554021737009, 45.43592541402861 ], [ -73.665558234480898, 45.435520460988293 ], [ -73.665274864731245, 45.435519575077379 ], [ -73.665273254390414, 45.435772607121798 ], [ -73.665145133767908, 45.435771908941341 ], [ -73.665145133767908, 45.435748141341286 ], [ -73.66455280450829, 45.435744913502532 ] ] ] } },
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{ "type": "Feature", "properties": { "id": "1647", "footprint_area": 2029.5591847320029, "floor_height": 3.9999999999999996, "year_built": 2019, "detailed_model_filename": "C:\\Users\\umroot\\Downloads\\Group 3\\hb_json\\Building_1647_e842fa1c.json", "type": "Building", "building_type": "1000", "number_of_stories": 8, "name": "Building_1647", "number_of_stories_above_ground": 8, "floor_area": 16236.473477856023, "maximum_roof_height": 31.999999999999996, "building_type_2": null, "building_type_3": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -73.66859722812778, 45.433658926667157 ], [ -73.668234175867511, 45.433673504539236 ], [ -73.66768744812434, 45.43401734227502 ], [ -73.668062079648976, 45.434307927076063 ], [ -73.668251686386938, 45.434187844642544 ], [ -73.668051314516219, 45.434031417950692 ], [ -73.668339905517598, 45.433849641065429 ], [ -73.668597133592854, 45.433839414337072 ], [ -73.66859722812778, 45.433658926667157 ] ] ] } },
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{ "type": "Feature", "properties": { "id": "1648", "footprint_area": 808.28076254848929, "floor_height": 3.9999999999999996, "year_built": 2019, "detailed_model_filename": "C:\\Users\\umroot\\Downloads\\Group 3\\hb_json\\Building_1648_1baa7890.json", "type": "Building", "building_type": "1000", "number_of_stories": 8, "name": "Building_1648", "number_of_stories_above_ground": 8, "floor_area": 6466.2461003879143, "maximum_roof_height": 31.999999999999996, "building_type_2": null, "building_type_3": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -73.668743078788893, 45.434565837770023 ], [ -73.668578717259948, 45.43467261555972 ], [ -73.668774017167109, 45.434819733874662 ], [ -73.66918929609129, 45.434819866014372 ], [ -73.66918952072686, 45.434666890634482 ], [ -73.668872628011471, 45.434666890634482 ], [ -73.668743078788893, 45.434565837770023 ] ] ] } },
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{ "type": "Feature", "properties": { "id": "1649", "footprint_area": 1458.239197322313, "floor_height": 3.9999999999999996, "year_built": 2019, "detailed_model_filename": "C:\\Users\\umroot\\Downloads\\Group 3\\hb_json\\Building_1649_57bc7dc1.json", "type": "Building", "building_type": "1000", "number_of_stories": 8, "name": "Building_1649", "number_of_stories_above_ground": 8, "floor_area": 11665.913578578504, "maximum_roof_height": 31.999999999999996, "building_type_2": null, "building_type_3": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -73.667010971728686, 45.434441574739452 ], [ -73.667114430089001, 45.434521723434102 ], [ -73.667260434022793, 45.434433564446572 ], [ -73.667326169895276, 45.434485171461255 ], [ -73.667520068466175, 45.434363261684247 ], [ -73.667721505074852, 45.434520773703198 ], [ -73.667912637656173, 45.434400698393027 ], [ -73.667539746318226, 45.434109118486703 ], [ -73.667010971728686, 45.434441574739452 ] ] ] } },
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{ "type": "Feature", "properties": { "id": "1650", "footprint_area": 504.15104012840311, "floor_height": 4.0, "year_built": 2019, "detailed_model_filename": "C:\\Users\\umroot\\Downloads\\Group 3\\hb_json\\Building_1650_287dcb62.json", "type": "Building", "building_type": "1000", "number_of_stories": 6, "name": "Building_1650", "number_of_stories_above_ground": 6, "floor_area": 3024.9062407704187, "maximum_roof_height": 24.0, "building_type_2": null, "building_type_3": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -73.668126468300898, 45.437361574191002 ], [ -73.668126416777696, 45.437235594637158 ], [ -73.667664100454218, 45.437235597612165 ], [ -73.667664102091791, 45.43736157422493 ], [ -73.668126468300898, 45.437361574191002 ] ] ] } },
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{ "type": "Feature", "properties": { "id": "1651", "footprint_area": 892.77340679971894, "floor_height": 4.0, "year_built": 2019, "detailed_model_filename": "C:\\Users\\umroot\\Downloads\\Group 3\\hb_json\\Building_1651_63dc1f59.json", "type": "Building", "building_type": "1000", "number_of_stories": 6, "name": "Building_1651", "number_of_stories_above_ground": 6, "floor_area": 5356.6404407983136, "maximum_roof_height": 24.0, "building_type_2": null, "building_type_3": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -73.668615557687048, 45.436113978949173 ], [ -73.668616133543296, 45.436293949755125 ], [ -73.66918885562572, 45.436293949755125 ], [ -73.669189050813969, 45.436113978949173 ], [ -73.668615557687048, 45.436113978949173 ] ] ] } },
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||||||
|
{ "type": "Feature", "properties": { "id": "1679", "footprint_area": 4319.0295927044426, "floor_height": 4.0, "year_built": 1990, "detailed_model_filename": "C:\\Users\\umroot\\Downloads\\Group 3\\hb_json\\Building_1679_b24420b9.json", "type": "Building", "building_type": "6111", "number_of_stories": 3, "name": "Building_1679", "number_of_stories_above_ground": 3, "floor_area": 12957.088778113328, "maximum_roof_height": 12.0, "building_type_2": 6000, "building_type_3": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -73.6676053228109, 45.435591523940779 ], [ -73.667602613827711, 45.436793618112581 ], [ -73.66801719364247, 45.436793649477039 ], [ -73.66802232750409, 45.435594547117901 ], [ -73.667654200178163, 45.435593080355837 ], [ -73.667654208542956, 45.435592048718291 ], [ -73.6676053228109, 45.435591523940779 ] ] ] } },
|
||||||
|
{ "type": "Feature", "properties": { "id": "1680", "footprint_area": 1225.15986339136, "floor_height": 4.0, "year_built": 1990, "detailed_model_filename": "C:\\Users\\umroot\\Downloads\\Group 3\\hb_json\\Building_1680_6c9bd455.json", "type": "Building", "building_type": "6111", "number_of_stories": 3, "name": "Building_1680", "number_of_stories_above_ground": 3, "floor_area": 3675.4795901740799, "maximum_roof_height": 12.0, "building_type_2": 6000, "building_type_3": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -73.667596622414578, 45.437020511364551 ], [ -73.667596622489881, 45.436793617659319 ], [ -73.666974026335765, 45.436790318252953 ], [ -73.666971576490013, 45.437017206599805 ], [ -73.667596622414578, 45.437020511364551 ] ] ] } },
|
||||||
|
{ "type": "Feature", "properties": { "id": "1681", "footprint_area": 7440.6678389157605, "floor_height": 4.0, "year_built": 1990, "detailed_model_filename": "C:\\Users\\umroot\\Downloads\\Group 3\\hb_json\\Building_1681_884eb763.json", "type": "Building", "building_type": "6111", "number_of_stories": 3, "name": "Building_1681", "number_of_stories_above_ground": 3, "floor_area": 22322.003516747282, "maximum_roof_height": 12.0, "building_type_2": 6000, "building_type_3": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -73.666492248633858, 45.43638661369733 ], [ -73.666473547611133, 45.43638661369733 ], [ -73.666482465287686, 45.435869205032546 ], [ -73.666486037344583, 45.435671228711151 ], [ -73.66649324539415, 45.434974010180255 ], [ -73.666403327005156, 45.434973140984212 ], [ -73.6660085967535, 45.434970382162497 ], [ -73.666006550487495, 45.436769134310026 ], [ -73.666479628079273, 45.436776655426591 ], [ -73.666492248633858, 45.43638661369733 ] ] ] } },
|
||||||
|
{ "type": "Feature", "properties": { "id": "1687", "footprint_area": 3264.7409803860646, "floor_height": 4.0, "year_built": 2019, "detailed_model_filename": "C:\\Users\\umroot\\Downloads\\Group 3\\hb_json\\Building_1687_cb4cf712.json", "type": "Building", "building_type": "1000", "number_of_stories": 11, "name": "Building_1687", "number_of_stories_above_ground": 11, "floor_area": 35912.150784246711, "maximum_roof_height": 44.0, "building_type_2": 1921, "building_type_3": 6111 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -73.665567583885291, 45.43466932058314 ], [ -73.665283112941253, 45.434850377542183 ], [ -73.665281874817651, 45.43496198922243 ], [ -73.665153485795187, 45.434961718577995 ], [ -73.665153808453496, 45.434932632276528 ], [ -73.664555714212526, 45.43531323855396 ], [ -73.664551889509383, 45.435609400125053 ], [ -73.664834024034789, 45.435611334713926 ], [ -73.66483749936549, 45.435362156046601 ], [ -73.665151281977501, 45.435160383553075 ], [ -73.665151792763695, 45.435114338318201 ], [ -73.665280184788216, 45.435114338338892 ], [ -73.665277174814051, 45.435385665207676 ], [ -73.665559627580862, 45.435386548251792 ], [ -73.665567583885291, 45.43466932058314 ] ] ] } },
|
||||||
|
{ "type": "Feature", "properties": { "id": "1674", "footprint_area": 2484.8825099908718, "floor_height": 4.0, "year_built": 1990, "detailed_model_filename": "C:\\Users\\umroot\\Downloads\\Group 3\\hb_json\\Building_1674_bbeef534.json", "type": "Building", "building_type": "6000", "number_of_stories": 3, "name": "Building_1674", "number_of_stories_above_ground": 3, "floor_area": 7454.6475299726153, "maximum_roof_height": 12.0, "building_type_2": 6111, "building_type_3": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -73.667248610160003, 45.435924163141479 ], [ -73.66724798100546, 45.435945580128184 ], [ -73.667549584180662, 45.435948734169607 ], [ -73.66754952022481, 45.435951888250706 ], [ -73.667604511663086, 45.435951888211008 ], [ -73.667605126160126, 45.43567920089707 ], [ -73.666884389888097, 45.435671430856793 ], [ -73.66688434878435, 45.435674445624294 ], [ -73.666486030839678, 45.435671772067714 ], [ -73.66648176823378, 45.435909648495979 ], [ -73.666563136490083, 45.435909648495979 ], [ -73.666563129588553, 45.435924001221537 ], [ -73.667248610160003, 45.435924163141479 ] ] ] } },
|
||||||
|
{ "type": "Feature", "properties": { "id": "1688", "footprint_area": 1641.9575463399633, "floor_height": 4.0, "year_built": 2019, "detailed_model_filename": "C:\\Users\\umroot\\Downloads\\Group 3\\hb_json\\Building_1688_55a8cadd.json", "type": "Building", "building_type": "1000", "number_of_stories": 14, "name": "Building_1688", "number_of_stories_above_ground": 14, "floor_area": 22987.405648759486, "maximum_roof_height": 56.0, "building_type_2": 6111, "building_type_3": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -73.665920466951619, 45.438074832810123 ], [ -73.666515593195911, 45.437963343188848 ], [ -73.666515593195911, 45.437725645247056 ], [ -73.66614588653529, 45.437725086993041 ], [ -73.666145886539852, 45.437636265029319 ], [ -73.665966143158769, 45.437636222120737 ], [ -73.665966572968443, 45.437725511285144 ], [ -73.665926803843391, 45.437725157245957 ], [ -73.665920466951619, 45.438074832810123 ] ] ] } },
|
||||||
|
{ "type": "Feature", "properties": { "id": "1675", "footprint_area": 3125.7782000612933, "floor_height": 4.0, "year_built": 1990, "detailed_model_filename": "C:\\Users\\umroot\\Downloads\\Group 3\\hb_json\\Building_1675_730b8065.json", "type": "Building", "building_type": "6000", "number_of_stories": 3, "name": "Building_1675", "number_of_stories_above_ground": 3, "floor_area": 9377.33460018388, "maximum_roof_height": 12.0, "building_type_2": 6111, "building_type_3": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -73.666490452451612, 45.434756728517236 ], [ -73.666493252099286, 45.434974531817161 ], [ -73.666486031717994, 45.435671772046057 ], [ -73.66688434878435, 45.435674445624294 ], [ -73.666888584086863, 45.435363806039966 ], [ -73.666893304291094, 45.434832713951032 ], [ -73.66680618457589, 45.43483247822946 ], [ -73.66680664677321, 45.434750015606156 ], [ -73.666571966084391, 45.434749376709796 ], [ -73.666571925317186, 45.434756728517236 ], [ -73.666490452451612, 45.434756728517236 ] ] ] } },
|
||||||
|
{ "type": "Feature", "properties": { "id": "1689", "footprint_area": 2187.8048172803792, "floor_height": 4.0, "year_built": 2019, "detailed_model_filename": "C:\\Users\\umroot\\Downloads\\Group 3\\hb_json\\Building_1689_909fbfb4.json", "type": "Building", "building_type": "1000", "number_of_stories": 15, "name": "Building_1689", "number_of_stories_above_ground": 15, "floor_area": 32817.072259205685, "maximum_roof_height": 60.0, "building_type_2": null, "building_type_3": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -73.667504385093551, 45.437594060949706 ], [ -73.667144840702818, 45.437660657392186 ], [ -73.667144834020434, 45.437688581026286 ], [ -73.667042118950661, 45.437707606377302 ], [ -73.667042099336527, 45.43721728784066 ], [ -73.666785074014001, 45.437217907319628 ], [ -73.666785208300354, 45.437912954554328 ], [ -73.667042081174898, 45.437865459625925 ], [ -73.667042087856515, 45.437837539218407 ], [ -73.667144802918216, 45.43781854754981 ], [ -73.667144796236599, 45.437846467957336 ], [ -73.667504385093537, 45.437779981190126 ], [ -73.667504385093551, 45.437594060949706 ] ] ] } },
|
||||||
|
{ "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 ] ] ] } }
|
||||||
|
]
|
||||||
|
}
|
||||||
117
main.py
117
main.py
|
|
@ -7,9 +7,31 @@ from hub.imports.usage_factory import UsageFactory
|
||||||
from hub.imports.weather_factory import WeatherFactory
|
from hub.imports.weather_factory import WeatherFactory
|
||||||
import hub.helpers.constants as cte
|
import hub.helpers.constants as cte
|
||||||
from hub.imports.energy_systems_factory import EnergySystemsFactory
|
from hub.imports.energy_systems_factory import EnergySystemsFactory
|
||||||
|
import geopandas as gpd
|
||||||
# Specify the GeoJSON file path
|
# Specify the GeoJSON file path
|
||||||
input_files_path = (Path(__file__).parent / 'input_files')
|
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_existing_year_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_existing_year_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_existing_year_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 = (Path(__file__).parent / 'out_files').resolve()
|
||||||
output_path.mkdir(parents=True, exist_ok=True)
|
output_path.mkdir(parents=True, exist_ok=True)
|
||||||
# Create city object from GeoJSON file
|
# Create city object from GeoJSON file
|
||||||
|
|
@ -24,15 +46,86 @@ ConstructionFactory('nrcan', city).enrich()
|
||||||
UsageFactory('nrcan', city).enrich()
|
UsageFactory('nrcan', city).enrich()
|
||||||
WeatherFactory('epw', city).enrich()
|
WeatherFactory('epw', city).enrich()
|
||||||
energy_plus_workflow(city)
|
energy_plus_workflow(city)
|
||||||
|
def to_dict(building, total_floor_area):
|
||||||
|
return {
|
||||||
|
'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' : building.heating_demand[cte.YEAR][0]/ (3.6e6 * total_floor_area),
|
||||||
|
'cooling_demand_kWh_per_m2' : building.cooling_demand[cte.YEAR][0]/ (3.6e6 * total_floor_area),
|
||||||
|
'domestic_hot_water_heat_demand_kWh_per_m2':building.domestic_hot_water_heat_demand[cte.YEAR][0]/ (3.6e6 * total_floor_area),
|
||||||
|
'appliances_electrical_demand_kWh_per_m2':building.appliances_electrical_demand[cte.YEAR][0]/ (3.6e6 * total_floor_area),
|
||||||
|
'lighting_electrical_demand_kWh_per_m2': building.lighting_electrical_demand[cte.YEAR][0]/ (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:
|
for building in city.buildings:
|
||||||
print(building.heating_demand[cte.YEAR][0] / 3.6e6)
|
|
||||||
print(building.name)
|
total_floor_area = 0
|
||||||
total_floor_area = 0
|
for thermal_zone in building.thermal_zones_from_internal_zones:
|
||||||
for thermal_zone in building.thermal_zones_from_internal_zones:
|
total_floor_area += thermal_zone.total_floor_area
|
||||||
total_floor_area += thermal_zone.total_floor_area
|
print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
|
||||||
print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
|
building.energy_systems_archetype_name = 'system 1 gas'
|
||||||
building.energy_systems_archetype_name = 'system 1 gas'
|
buildings_dic[building.name] = to_dict(building,total_floor_area)
|
||||||
print('test')
|
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()
|
EnergySystemsFactory('montreal_custom', city).enrich()
|
||||||
print('test')
|
print('test')
|
||||||
for building in city.buildings:
|
for building in city.buildings:
|
||||||
|
|
@ -54,4 +147,10 @@ for building in city.buildings:
|
||||||
# total_floor_area = 0
|
# total_floor_area = 0
|
||||||
# for thermal_zone in building.thermal_zones_from_internal_zones:
|
# for thermal_zone in building.thermal_zones_from_internal_zones:
|
||||||
# total_floor_area += thermal_zone.total_floor_area
|
# total_floor_area += thermal_zone.total_floor_area
|
||||||
# print(building.heating_demand[cte.YEAR][0] / (3.6e6 * 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)
|
||||||
145
processor.py
Normal file
145
processor.py
Normal file
|
|
@ -0,0 +1,145 @@
|
||||||
|
import json
|
||||||
|
from pathlib import Path
|
||||||
|
import pandas as pd
|
||||||
|
import geopandas as gpd
|
||||||
|
# Specify the GeoJSON file path
|
||||||
|
input_files_path = (Path(__file__).parent / 'input_files')
|
||||||
|
geojson_file_path = input_files_path / 'Lachine_moved_existing_year_type.geojson'
|
||||||
|
#read it to use later
|
||||||
|
gdf = gpd.read_file(geojson_file_path)
|
||||||
|
#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
|
||||||
|
|
||||||
|
gdf_building_outputs={}
|
||||||
|
|
||||||
|
output_paths = {}
|
||||||
|
# Create directories for the output folders
|
||||||
|
for folder in output_folders:
|
||||||
|
path = output_path / folder
|
||||||
|
path.mkdir(parents=True, exist_ok=True)
|
||||||
|
output_paths[folder] = path
|
||||||
|
# Read the resulting GeoDataFrames saved in each folder
|
||||||
|
gdf_building_outputs = {}
|
||||||
|
for folder in output_folders:
|
||||||
|
path = output_paths[folder] / 'updated_buildings.geojson'
|
||||||
|
if path.exists():
|
||||||
|
gdf_building_outputs[folder] = gpd.read_file(path)
|
||||||
|
else:
|
||||||
|
gdf_building_outputs[folder] = gpd.GeoDataFrame()
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
percentage_data = {
|
||||||
|
1646: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1647: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1648: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1649: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1650: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1651: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1652: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1653: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1654: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1655: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1656: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1657: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1658: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1659: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1660: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1661: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1662: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1663: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1664: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1665: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1666: {"type1_%": 0.891045711, "type2_%": 0.108954289, "type3_%": 0},
|
||||||
|
1667: {"type1_%": 0.8, "type2_%": 0.2, "type3_%": 0},
|
||||||
|
1668: {"type1_%": 0.666666667, "type2_%": 0.333333333, "type3_%": 0},
|
||||||
|
1669: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1673: {"type1_%": 0.666666667, "type2_%": 0.3, "type3_%": 0},
|
||||||
|
1674: {"type1_%": 0.666666667, "type2_%": 0.3, "type3_%": 0},
|
||||||
|
1675: {"type1_%": 0.666666667, "type2_%": 0.3, "type3_%": 0},
|
||||||
|
1676: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1677: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||||
|
1678: {"type1_%": 0.666666667, "type2_%": 0.3, "type3_%": 0},
|
||||||
|
1679: {"type1_%": 0.666666667, "type2_%": 0.3, "type3_%": 0},
|
||||||
|
1680: {"type1_%": 0.666666667, "type2_%": 0.3, "type3_%": 0},
|
||||||
|
1681: {"type1_%": 0.666666667, "type2_%": 0.3, "type3_%": 0},
|
||||||
|
1687: {"type1_%": 0.606611029, "type2_%": 0.28211422, "type3_%": 0.11127475},
|
||||||
|
1688: {"type1_%": 0.92, "type2_%": 0.1, "type3_%": 0},
|
||||||
|
1689: {"type1_%": 0.96, "type2_%": 0, "type3_%": 0},
|
||||||
|
1690: {"type1_%": 0.94, "type2_%": 0.1, "type3_%": 0},
|
||||||
|
1691: {"type1_%": 0.75, "type2_%": 0.3, "type3_%": 0},
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
# Define a function to calculate new demands based on percentages
|
||||||
|
def calculate_demands(building_id, percentages, gdf_building_type, gdf_building_type_2, gdf_building_type_3):
|
||||||
|
new_demands = {}
|
||||||
|
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_type_1 = gdf_building_type.loc[gdf_building_type['id'] == building_id, demand_type].values[
|
||||||
|
0] if not gdf_building_type.empty else 0
|
||||||
|
demand_type_2 = gdf_building_type_2.loc[gdf_building_type_2['id'] == building_id, demand_type].values[
|
||||||
|
0] if not gdf_building_type_2.empty else 0
|
||||||
|
demand_type_3 = gdf_building_type_3.loc[gdf_building_type_3['id'] == building_id, demand_type].values[
|
||||||
|
0] if not gdf_building_type_3.empty else 0
|
||||||
|
|
||||||
|
new_demand = (
|
||||||
|
demand_type_1 * percentages['type1_%'] +
|
||||||
|
demand_type_2 * percentages['type2_%'] +
|
||||||
|
demand_type_3 * percentages['type3_%']
|
||||||
|
)
|
||||||
|
new_demands[demand_type] = new_demand
|
||||||
|
return new_demands
|
||||||
|
|
||||||
|
|
||||||
|
# Process each building in the GeoJSON
|
||||||
|
for idx, feature in gdf.iterrows():
|
||||||
|
building_id = feature['id'] # Adjust this based on your GeoJSON structure
|
||||||
|
if building_id == 1673:
|
||||||
|
percentages = percentage_data[building_id]
|
||||||
|
print(percentages)
|
||||||
|
new_demands = calculate_demands(building_id, percentages,
|
||||||
|
gdf_building_outputs.get('building_type', gpd.GeoDataFrame()),
|
||||||
|
gdf_building_outputs.get('building_type_2', gpd.GeoDataFrame()),
|
||||||
|
gdf_building_outputs.get('building_type_3', gpd.GeoDataFrame()))
|
||||||
|
print(new_demands)
|
||||||
|
# Update the properties of the feature
|
||||||
|
gdf.at[idx, 'heating_demand_kWh'] = new_demands['heating_demand_kWh']
|
||||||
|
gdf.at[idx, 'cooling_demand_kWh'] = new_demands['cooling_demand_kWh']
|
||||||
|
gdf.at[idx, 'domestic_hot_water_heat_demand_kWh'] = new_demands['domestic_hot_water_heat_demand_kWh']
|
||||||
|
gdf.at[idx, 'appliances_electrical_demand_kWh'] = new_demands['appliances_electrical_demand_kWh']
|
||||||
|
gdf.at[idx, 'lighting_electrical_demand_kWh'] = new_demands['lighting_electrical_demand_kWh']
|
||||||
|
|
||||||
|
print('test')
|
||||||
|
|
||||||
|
#
|
||||||
|
# if building_id in percentage_data:
|
||||||
|
# percentages = percentage_data[building_id]
|
||||||
|
# new_demands = calculate_demands(building_id, percentages,
|
||||||
|
# gdf_building_outputs.get('building_type', gpd.GeoDataFrame()),
|
||||||
|
# gdf_building_outputs.get('building_type_2', gpd.GeoDataFrame()),
|
||||||
|
# gdf_building_outputs.get('building_type_3', gpd.GeoDataFrame()))
|
||||||
|
#
|
||||||
|
# # Update the properties of each feature
|
||||||
|
# gdf.at[idx, 'heating_demand_kWh'] = new_demands['heating_demand_kWh']
|
||||||
|
# gdf.at[idx, 'cooling_demand_kWh'] = new_demands['cooling_demand_kWh']
|
||||||
|
# gdf.at[idx, 'domestic_hot_water_heat_demand_kWh'] = new_demands['domestic_hot_water_heat_demand_kWh']
|
||||||
|
# gdf.at[idx, 'appliances_electrical_demand_kWh'] = new_demands['appliances_electrical_demand_kWh']
|
||||||
|
# gdf.at[idx, 'lighting_electrical_demand_kWh'] = new_demands['lighting_electrical_demand_kWh']
|
||||||
|
#
|
||||||
|
# # Save the updated GeoDataFrame to the appropriate folder
|
||||||
|
# for folder, key in zip(output_folders, ['type1_%', 'type2_%', 'type3_%']):
|
||||||
|
# if percentages[key] > 0:
|
||||||
|
# output_gdf = gdf_building_outputs[folder]
|
||||||
|
# output_gdf = output_gdf.append(gdf.iloc[[idx]], ignore_index=True)
|
||||||
|
# output_gdf.to_file(output_paths[folder] / 'buildings.geojson', driver='GeoJSON')
|
||||||
|
#
|
||||||
113
updated_buildings.geojson
Normal file
113
updated_buildings.geojson
Normal file
|
|
@ -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": [
|
||||||
|
|
||||||
Loading…
Reference in New Issue
Block a user