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Andrea Gabaldon Moreno 2024-08-01 10:16:52 -04:00
parent 1488c9dbce
commit 28c88e2978
4 changed files with 266 additions and 273 deletions
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2
hub/data/energy_systems/montreal_custom_systems.xml
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@ -198,7 +198,7 @@
<equipments> <equipments>
<generation_id>3</generation_id> <generation_id>3</generation_id>
<distribution_id>8</distribution_id> <distribution_id>8</distribution_id>
g </equipments> </equipments>
</system> </system>
<system id="5"> <system id="5">
<name>Single zone packaged rooftop unit with electrical resistance furnace and baseboards and fuel boiler for acs</name> <name>Single zone packaged rooftop unit with electrical resistance furnace and baseboards and fuel boiler for acs</name>

221
main.py
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@ -1,5 +1,6 @@
from pathlib import Path from pathlib import Path
from scripts.ep_workflow import energy_plus_workflow from scripts.ep_workflow import energy_plus_workflow
from hub.helpers.monthly_values import MonthlyValues
from hub.imports.geometry_factory import GeometryFactory from hub.imports.geometry_factory import GeometryFactory
from hub.helpers.dictionaries import Dictionaries from hub.helpers.dictionaries import Dictionaries
from hub.imports.construction_factory import ConstructionFactory from hub.imports.construction_factory import ConstructionFactory
@ -7,36 +8,24 @@ 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
from hub.helpers.peak_loads import PeakLoads
import geopandas as gpd import geopandas as gpd
import json
# 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')
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 = (Path(__file__).parent / 'out_files').resolve()
output_path.mkdir(parents=True, exist_ok=True) output_path.mkdir(parents=True, exist_ok=True)
geojson_file_path_baseline = output_path / 'updated_buildings_with_all_data_baseline.geojson'
geojson_file_path_2024 = output_path / 'updated_buildings_with_all_data.geojson'
with open(geojson_file_path_baseline , 'r') as f:
building_type_data = json.load(f)
with open(geojson_file_path_2024, 'r') as f:
building_type_data_2024 = json.load(f)
# Create city object from GeoJSON file # Create city object from GeoJSON file
city = GeometryFactory('geojson', city = GeometryFactory('geojson',
path=geojson_file_path, path=geojson_file_path_baseline,
height_field='maximum_roof_height', height_field='maximum_roof_height',
year_of_construction_field='year_built', year_of_construction_field='year_built',
function_field='building_type', function_field='building_type',
@ -45,34 +34,97 @@ city = GeometryFactory('geojson',
ConstructionFactory('nrcan', city).enrich() 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 is not going to be processed here, as demand has been obtained before
# energy_plus_workflow(city)
#SRA algorithm
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)
print('test')
for building in city.buildings:
building.energy_systems_archetype_name = 'system 1 electricity pv'
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): def to_dict(building, total_floor_area):
return { return {
'roof_area': building.floor_area, 'roof_area': building.floor_area,
'total_floor_area': total_floor_area, 'total_floor_area': total_floor_area,
'year_of_construction' : building.year_of_construction, 'heating_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.energy_consumption_breakdown[cte.HOUR]],
'type_function':building.function, 'cooling_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.cooling_demand[cte.HOUR]],
'beam_kWh_per_m2': sum(building.beam[cte.HOUR])/ (3.6e6), 'domestic_hot_water_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.domestic_hot_water_heat_demand[cte.HOUR]],
'diffuse_kWh_per_m2': sum(building.diffuse[cte.HOUR])/ (3.6e6), 'appliances_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.appliances_electrical_demand[cte.HOUR]],
'direct_normal_kWh_per_m2': sum(building.direct_normal[cte.HOUR])/ (3.6e6), 'lighting_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.lighting_electrical_demand[cte.HOUR]]
'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={} buildings_dic={}
@ -82,78 +134,15 @@ 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))
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: # for building in city.buildings:
# print(building.heating_demand[cte.YEAR][0] / 3.6e6)
# print(building.name) # print(building.name)
# print(building.year_of_construction) # total_floor_area = 0
# print(building.usages_percentage) # 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))

155
main_results_in_geojson.py
View File

@ -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))

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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)