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 2024-07-17 18:39:52 -04:00
parent 73a7a12a97
commit 76a6f5f930
9 changed files with 413 additions and 11848 deletions

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@ -1,86 +0,0 @@
"""
Dictionaries module for hub function to nrcan construction function
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Project Coder Guille Gutierrez Guillermo.GutierrezMorote@concordia.ca
"""
import hub.helpers.constants as cte
class HubFunctionToNrcanConstructionFunction:
"""
Hub function to nrcan construction function class
"""
def __init__(self):
self._dictionary = {
cte.RESIDENTIAL: 'MidriseApartment',
cte.SINGLE_FAMILY_HOUSE: 'MidriseApartment',
cte.MULTI_FAMILY_HOUSE: 'HighriseApartment',
cte.ROW_HOUSE: 'MidriseApartment',
cte.MID_RISE_APARTMENT: 'MidriseApartment',
cte.HIGH_RISE_APARTMENT: 'HighriseApartment',
cte.OFFICE_AND_ADMINISTRATION: 'MediumOffice',
cte.SMALL_OFFICE: 'SmallOffice',
cte.MEDIUM_OFFICE: 'MediumOffice',
cte.LARGE_OFFICE: 'LargeOffice',
cte.COURTHOUSE: 'MediumOffice',
cte.FIRE_STATION: 'n/a',
cte.PENITENTIARY: 'LargeHotel',
cte.POLICE_STATION: 'n/a',
cte.POST_OFFICE: 'MediumOffice',
cte.LIBRARY: 'MediumOffice',
cte.EDUCATION: 'SecondarySchool',
cte.PRIMARY_SCHOOL: 'PrimarySchool',
cte.PRIMARY_SCHOOL_WITH_SHOWER: 'PrimarySchool',
cte.SECONDARY_SCHOOL: 'SecondarySchool',
cte.UNIVERSITY: 'SecondarySchool',
cte.LABORATORY_AND_RESEARCH_CENTER: 'SecondarySchool',
cte.STAND_ALONE_RETAIL: 'RetailStandalone',
cte.HOSPITAL: 'Hospital',
cte.OUT_PATIENT_HEALTH_CARE: 'Outpatient',
cte.HEALTH_CARE: 'Outpatient',
cte.RETIREMENT_HOME_OR_ORPHANAGE: 'SmallHotel',
cte.COMMERCIAL: 'RetailStripmall',
cte.STRIP_MALL: 'RetailStripmall',
cte.SUPERMARKET: 'RetailStripmall',
cte.RETAIL_SHOP_WITHOUT_REFRIGERATED_FOOD: 'RetailStandalone',
cte.RETAIL_SHOP_WITH_REFRIGERATED_FOOD: 'RetailStandalone',
cte.RESTAURANT: 'FullServiceRestaurant',
cte.QUICK_SERVICE_RESTAURANT: 'QuickServiceRestaurant',
cte.FULL_SERVICE_RESTAURANT: 'FullServiceRestaurant',
cte.HOTEL: 'SmallHotel',
cte.HOTEL_MEDIUM_CLASS: 'SmallHotel',
cte.SMALL_HOTEL: 'SmallHotel',
cte.LARGE_HOTEL: 'LargeHotel',
cte.DORMITORY: 'SmallHotel',
cte.EVENT_LOCATION: 'n/a',
cte.CONVENTION_CENTER: 'n/a',
cte.HALL: 'n/a',
cte.GREEN_HOUSE: 'n/a',
cte.INDUSTRY: 'n/a',
cte.WORKSHOP: 'n/a',
cte.WAREHOUSE: 'Warehouse',
cte.WAREHOUSE_REFRIGERATED: 'Warehouse',
cte.SPORTS_LOCATION: 'n/a',
cte.SPORTS_ARENA: 'n/a',
cte.GYMNASIUM: 'n/a',
cte.MOTION_PICTURE_THEATRE: 'n/a',
cte.MUSEUM: 'n/a',
cte.PERFORMING_ARTS_THEATRE: 'n/a',
cte.TRANSPORTATION: 'n/a',
cte.AUTOMOTIVE_FACILITY: 'n/a',
cte.PARKING_GARAGE: 'n/a',
cte.RELIGIOUS: 'n/a',
cte.NON_HEATED: 'n/a',
cte.DATACENTER: 'n/a',
cte.FARM: 'n/a'
}
@property
def dictionary(self) -> dict:
"""
Get the dictionary
:return: {}
"""
return self._dictionary

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@ -1,105 +0,0 @@
"""
NrcanPhysicsParameters import the construction and material information defined by NRCAN
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Project Coder Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
"""
import logging
from hub.catalog_factories.construction_catalog_factory import ConstructionCatalogFactory
from hub.city_model_structure.building_demand.thermal_archetype import ThermalArchetype
from hub.city_model_structure.building_demand.construction import Construction
from hub.city_model_structure.building_demand.layer import Layer
from hub.helpers.dictionaries import Dictionaries
from hub.imports.construction.helpers.construction_helper import ConstructionHelper
class NrcanPhysicsParameters:
"""
NrcanPhysicsParameters class
"""
def __init__(self, city, divide_in_storeys=False):
self._city = city
self._divide_in_storeys = divide_in_storeys
self._climate_zone = ConstructionHelper.city_to_nrcan_climate_zone(city.climate_reference_city)
def enrich_buildings(self):
"""
Returns the city with the construction parameters assigned to the buildings
"""
city = self._city
nrcan_catalog = ConstructionCatalogFactory('nrcan').catalog
for building in city.buildings:
if building.function not in Dictionaries().hub_function_to_nrcan_construction_function:
logging.error('Building %s has an unknown building function %s', building.name, building.function)
continue
function = Dictionaries().hub_function_to_nrcan_construction_function[building.function]
try:
archetype = self._search_archetype(nrcan_catalog, function, building.year_of_construction, self._climate_zone)
except KeyError:
logging.error('Building %s has unknown construction archetype for building function: %s '
'[%s], building year of construction: %s and climate zone %s', building.name, function,
building.function, building.year_of_construction, self._climate_zone)
continue
thermal_archetype = ThermalArchetype()
self._assign_values(thermal_archetype, archetype)
for internal_zone in building.internal_zones:
internal_zone.thermal_archetype = thermal_archetype
@staticmethod
def _search_archetype(nrcan_catalog, function, year_of_construction, climate_zone):
nrcan_archetypes = nrcan_catalog.entries('archetypes')
for building_archetype in nrcan_archetypes:
construction_period_limits = building_archetype.construction_period.split('_')
if int(construction_period_limits[0]) <= int(year_of_construction) <= int(construction_period_limits[1]):
if str(function) == str(building_archetype.function) and climate_zone == str(building_archetype.climate_zone):
return building_archetype
raise KeyError('archetype not found')
@staticmethod
def _assign_values(thermal_archetype, catalog_archetype):
thermal_archetype.average_storey_height = catalog_archetype.average_storey_height
thermal_archetype.extra_loses_due_to_thermal_bridges = catalog_archetype.extra_loses_due_to_thermal_bridges
thermal_archetype.thermal_capacity = catalog_archetype.thermal_capacity
thermal_archetype.indirect_heated_ratio = 0
thermal_archetype.infiltration_rate_for_ventilation_system_on = catalog_archetype.infiltration_rate_for_ventilation_system_on
thermal_archetype.infiltration_rate_for_ventilation_system_off = catalog_archetype.infiltration_rate_for_ventilation_system_off
_constructions = []
for catalog_construction in catalog_archetype.constructions:
construction = Construction()
construction.type = catalog_construction.type
construction.name = catalog_construction.name
if catalog_construction.window_ratio is not None:
for _orientation in catalog_construction.window_ratio:
if catalog_construction.window_ratio[_orientation] is None:
catalog_construction.window_ratio[_orientation] = 0
construction.window_ratio = catalog_construction.window_ratio
_layers = []
for layer_archetype in catalog_construction.layers:
layer = Layer()
layer.thickness = layer_archetype.thickness
archetype_material = layer_archetype.material
layer.material_name = archetype_material.name
layer.no_mass = archetype_material.no_mass
if archetype_material.no_mass:
layer.thermal_resistance = archetype_material.thermal_resistance
else:
layer.density = archetype_material.density
layer.conductivity = archetype_material.conductivity
layer.specific_heat = archetype_material.specific_heat
layer.solar_absorptance = archetype_material.solar_absorptance
layer.thermal_absorptance = archetype_material.thermal_absorptance
layer.visible_absorptance = archetype_material.visible_absorptance
_layers.append(layer)
construction.layers = _layers
if catalog_construction.window is not None:
window_archetype = catalog_construction.window
construction.window_frame_ratio = window_archetype.frame_ratio
construction.window_g_value = window_archetype.g_value
construction.window_overall_u_value = window_archetype.overall_u_value
_constructions.append(construction)
thermal_archetype.constructions = _constructions

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@ -135,6 +135,8 @@ class Geojson:
building_aliases = []
if 'id' in feature:
building_name = feature['id']
elif 'id' in feature['properties']:
building_name = feature['properties']['id']
else:
building_name = uuid.uuid4()
if self._aliases_field is not None:

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@ -0,0 +1,45 @@
{
"type": "FeatureCollection",
"name": "Lachine_moved_existing_year_type",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
{ "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 ] ] ] } },
{ "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": "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
View File

@ -7,9 +7,31 @@ from hub.imports.usage_factory import UsageFactory
from hub.imports.weather_factory import WeatherFactory
import hub.helpers.constants as cte
from hub.imports.energy_systems_factory import EnergySystemsFactory
import geopandas as gpd
# Specify the GeoJSON file path
input_files_path = (Path(__file__).parent / 'input_files')
geojson_file_path = input_files_path / 'test.geojson'
building_type_2_modelling=2
#'Lachine_New_Developments.geojson'
geojson_file_path = input_files_path / 'Lachine_moved_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.mkdir(parents=True, exist_ok=True)
# Create city object from GeoJSON file
@ -24,15 +46,86 @@ ConstructionFactory('nrcan', city).enrich()
UsageFactory('nrcan', city).enrich()
WeatherFactory('epw', city).enrich()
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:
print(building.heating_demand[cte.YEAR][0] / 3.6e6)
print(building.name)
total_floor_area = 0
for thermal_zone in building.thermal_zones_from_internal_zones:
total_floor_area += thermal_zone.total_floor_area
print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
building.energy_systems_archetype_name = 'system 1 gas'
total_floor_area = 0
for thermal_zone in building.thermal_zones_from_internal_zones:
total_floor_area += thermal_zone.total_floor_area
print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
building.energy_systems_archetype_name = 'system 1 gas'
buildings_dic[building.name] = to_dict(building,total_floor_area)
print('test')
""""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:
@ -54,4 +147,10 @@ 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))
# 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
View 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
View 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,
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],
[
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],
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],
[
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],
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],
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],
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],
[
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]
]
]
},
"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": [