76 lines
3.9 KiB
Python
76 lines
3.9 KiB
Python
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import pandas as pd
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from scripts.geojson_creator import process_geojson
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from pathlib import Path
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import subprocess
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from scripts.ep_run_enrich import energy_plus_workflow
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from hub.imports.geometry_factory import GeometryFactory
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from hub.helpers.dictionaries import Dictionaries
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from hub.imports.construction_factory import ConstructionFactory
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from hub.imports.usage_factory import UsageFactory
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from hub.imports.weather_factory import WeatherFactory
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from hub.imports.results_factory import ResultFactory
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from scripts import random_assignation
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from hub.imports.energy_systems_factory import EnergySystemsFactory
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from scripts.energy_system_sizing_and_simulation_factory import EnergySystemsSimulationFactory
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from scripts.costs.cost import Cost
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from scripts.costs.constants import SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT_AND_PV
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import hub.helpers.constants as cte
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from hub.exports.exports_factory import ExportsFactory
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from scripts.solar_angles import CitySolarAngles
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from scripts.pv_sizing_and_simulation import PVSizingSimulation
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# Specify the GeoJSON file path
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location = [45.49034212153445, -73.61435648647083]
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geojson_file = process_geojson(x=location[1], y=location[0], diff=0.0001)
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file_path = (Path(__file__).parent / 'input_files' / 'output_buildings.geojson')
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# Specify the output path for the PDF file
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output_path = (Path(__file__).parent / 'out_files').resolve()
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# Create city object from GeoJSON file
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city = GeometryFactory('geojson',
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path=file_path,
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height_field='height',
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year_of_construction_field='year_of_construction',
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function_field='function',
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function_to_hub=Dictionaries().montreal_function_to_hub_function).city
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# Enrich city data
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ConstructionFactory('nrcan', city).enrich()
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UsageFactory('nrcan', city).enrich()
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WeatherFactory('epw', city).enrich()
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ResultFactory('energy_plus_multiple_buildings', city, output_path).enrich()
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ExportsFactory('sra', city, output_path).export()
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sra_path = (output_path / f'{city.name}_sra.xml').resolve()
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subprocess.run(['sra', str(sra_path)])
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ResultFactory('sra', city, output_path).enrich()
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solar_angles = CitySolarAngles(city.name,
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city.latitude,
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city.longitude,
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tilt_angle=45,
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surface_azimuth_angle=180).calculate
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random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
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EnergySystemsFactory('montreal_future', city).enrich()
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for building in city.buildings:
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EnergySystemsSimulationFactory('archetype13', building=building, output_path=output_path).enrich()
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ghi = [x / cte.WATTS_HOUR_TO_JULES for x in building.roofs[0].global_irradiance[cte.HOUR]]
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pv_sizing_simulation = PVSizingSimulation(building,
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solar_angles,
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tilt_angle=45,
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module_height=1,
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module_width=2,
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ghi=ghi)
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for building in city.buildings:
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costs = Cost(building=building, retrofit_scenario=SYSTEM_RETROFIT_AND_PV).life_cycle
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costs.to_csv(output_path / f'{building.name}_lcc.csv')
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costs.loc['global_capital_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].to_csv(
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output_path / f'{building.name}_cc.csv')
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# (costs.loc['global_operational_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].
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# to_csv(output_path / f'{building.name}_op.csv'))
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# costs.loc['global_maintenance_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].to_csv(
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# output_path / f'{building.name}_m.csv')
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print(building.name)
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investment_cost = costs.loc['global_capital_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].loc[0, 'D3020_heat_and_cooling_generating_systems']
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lcc_capex = costs.loc['total_capital_costs_systems', f'Scenario {SYSTEM_RETROFIT_AND_PV}']
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print(investment_cost)
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print(lcc_capex)
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