218 lines
14 KiB
Python
218 lines
14 KiB
Python
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
|
|
from pathlib import Path
|
|
import subprocess
|
|
from hub.imports.results_factory import ResultFactory
|
|
from hub.imports.energy_systems_factory import EnergySystemsFactory
|
|
from scripts.energy_system_sizing_and_simulation_factory import EnergySystemsSimulationFactory
|
|
import hub.helpers.constants as cte
|
|
from hub.exports.exports_factory import ExportsFactory
|
|
from scripts.solar_angles import CitySolarAngles
|
|
from scripts.radiation_tilted import RadiationTilted
|
|
|
|
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)
|
|
energy_plus_output_path = output_path / 'energy_plus_outputs'
|
|
energy_plus_output_path.mkdir(parents=True, exist_ok=True)
|
|
simulation_results_path = (Path(__file__).parent / 'out_files' / 'simulation_results').resolve()
|
|
simulation_results_path.mkdir(parents=True, exist_ok=True)
|
|
sra_output_path = output_path / 'sra_outputs'
|
|
sra_output_path.mkdir(parents=True, exist_ok=True)
|
|
cost_analysis_output_path = output_path / 'cost_analysis'
|
|
cost_analysis_output_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
#%%-----------------------------------------------
|
|
#"""add geojson paths and create city for Baseline"""
|
|
#%% # -----------------------------------------------
|
|
|
|
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
|
|
city = GeometryFactory('geojson',
|
|
path=geojson_file_path_baseline,
|
|
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)
|
|
#%%-----------------------------------------------
|
|
# """SRA"""
|
|
#%% # -----------------------------------------------
|
|
ExportsFactory('sra', city, sra_output_path).export()
|
|
sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
|
|
subprocess.run(['sra', str(sra_path)])
|
|
ResultFactory('sra', city, sra_output_path).enrich()
|
|
solar_angles = CitySolarAngles(city.name,
|
|
city.latitude,
|
|
city.longitude,
|
|
tilt_angle=45,
|
|
surface_azimuth_angle=180).calculate
|
|
for building in city.buildings:
|
|
ghi = [x / cte.WATTS_HOUR_TO_JULES for x in building.roofs[0].global_irradiance[cte.HOUR]]
|
|
RadiationTilted(building,
|
|
solar_angles,
|
|
tilt_angle=45,
|
|
ghi=ghi).enrich()
|
|
# building_names = []
|
|
# for building in city.buildings:
|
|
# building_names.append(building.name)
|
|
#
|
|
# df = pd.DataFrame(columns=building_names)
|
|
# df1 = pd.DataFrame(columns=building_names)
|
|
# print('test')
|
|
# for building in city.buildings:
|
|
# # if building.name in selected_buildings_list:
|
|
# df[f'{building.name}'] = building.roofs[0].global_irradiance[cte.HOUR]
|
|
# df1[f'{building.name}'] = building.roofs[0].global_irradiance_tilted[cte.HOUR]
|
|
#
|
|
# df.to_csv('solar_radiation_horizontal_selected_buildings.csv')
|
|
# df1.to_csv('solar_radiation_tilted_selected_buildings.csv')
|
|
|
|
#%% # -----------------------------------------------
|
|
#"""Enrich city with geojson file data"""
|
|
#%% # -----------------------------------------------
|
|
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')
|
|
|
|
#%%-----------------------------------------------
|
|
# """ADD energy systems"""
|
|
#%% # -----------------------------------------------
|
|
|
|
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):
|
|
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
|
|
|
|
|
|
|
|
|
|
# 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:
|
|
# building.energy_systems_archetype_name = 'PV+4Pipe+DHW'
|
|
# EnergySystemsFactory('montreal_future', city).enrich()
|
|
# for building in city.buildings:
|
|
# EnergySystemsSimulationFactory('archetype13', building=building, output_path=simulation_results_path).enrich() |