70 lines
3.5 KiB
Python
70 lines
3.5 KiB
Python
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 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.solar_angles import CitySolarAngles
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from scripts.ep_run_enrich import energy_plus_workflow
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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.pv_sizing_and_simulation import PVSizingSimulation
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# Specify the GeoJSON file path
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# geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.0001)
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file_path = (Path(__file__).parent / 'input_files' / 'output_buildings1.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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ExportsFactory('obj', city, output_path).export()
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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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energy_plus_workflow(city)
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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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df = pd.DataFrame()
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df.index = ['yearly lighting (kWh)', 'yearly appliance (kWh)', 'yearly heating (kWh)', 'yearly cooling (kWh)',
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'yearly dhw (kWh)', 'roof area (m2)', 'used area for pv (m2)', 'number of panels', 'pv production (kWh)']
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for building in city.buildings:
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ghi = [x 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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pv_sizing_simulation.pv_output()
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yearly_lighting = building.lighting_electrical_demand[cte.YEAR][0] / 1000
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yearly_appliance = building.appliances_electrical_demand[cte.YEAR][0] / 1000
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yearly_heating = building.heating_demand[cte.YEAR][0] / (3.6e6 * 3)
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yearly_cooling = building.cooling_demand[cte.YEAR][0] / (3.6e6 * 4.5)
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yearly_dhw = building.domestic_hot_water_heat_demand[cte.YEAR][0] / 1000
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roof_area = building.roofs[0].perimeter_area
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used_roof = pv_sizing_simulation.available_space()
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number_of_pv_panels = pv_sizing_simulation.total_number_of_panels
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yearly_pv = building.onsite_electrical_production[cte.YEAR][0] / 1000
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df[f'{building.name}'] = [yearly_lighting, yearly_appliance, yearly_heating, yearly_cooling, yearly_dhw, roof_area,
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used_roof, number_of_pv_panels, yearly_pv]
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df.to_csv(output_path / 'pv.csv')
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