2024-11-26 05:43:11 -05:00
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from hub.imports.energy_systems_factory import EnergySystemsFactory
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2024-10-03 18:28:35 -04:00
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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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2024-10-09 12:11:39 -04:00
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from hub.imports.construction_factory import ConstructionFactory
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from hub.imports.results_factory import ResultFactory
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2024-11-26 05:43:11 -05:00
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from hub.exports.exports_factory import ExportsFactory
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import subprocess
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from pathlib import Path
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from hub.imports.weather_factory import WeatherFactory
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from pv_assessment.electricity_demand_calculator import HourlyElectricityDemand
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from pv_assessment.pv_system_assessment import PvSystemAssessment
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from pv_assessment.solar_calculator import SolarCalculator
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2024-10-03 18:28:35 -04:00
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2024-10-09 12:11:39 -04:00
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input_file = "data/cmm_test_corrected.geojson"
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demand_file = "data/energy_demand_data.csv"
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2024-11-26 05:43:11 -05:00
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# Define specific paths for outputs from SRA (Simplified Radiosity Algorith) and PV calculation processes
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output_path = (Path(__file__).parent.parent / 'out_files').resolve()
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output_path.mkdir(parents=True, exist_ok=True)
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sra_output_path = output_path / 'sra_outputs'
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sra_output_path.mkdir(parents=True, exist_ok=True)
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pv_assessment_path = output_path / 'pv_outputs'
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pv_assessment_path.mkdir(parents=True, exist_ok=True)
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city = GeometryFactory(
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2024-10-09 12:11:39 -04:00
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"geojson",
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input_file,
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height_field="height",
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year_of_construction_field="contr_year",
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function_field="function_c",
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adjacency_field="adjacency",
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function_to_hub=Dictionaries().montreal_function_to_hub_function).city
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ConstructionFactory('nrcan', city).enrich()
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WeatherFactory('epw', city).enrich()
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ResultFactory('archetypes', city, demand_file).enrich()
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# Export the city data in SRA-compatible format to facilitate solar radiation assessment
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ExportsFactory('sra', city, sra_output_path).export()
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# Run SRA simulation using an external command, passing the generated SRA XML file path as input
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sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
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subprocess.run(['sra', str(sra_path)])
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# Enrich city data with SRA simulation results for subsequent analysis
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ResultFactory('sra', city, sra_output_path).enrich()
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# # Initialize solar calculation parameters (e.g., azimuth, altitude) and compute irradiance and solar angles
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tilt_angle = 37
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solar_parameters = SolarCalculator(city=city,
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surface_azimuth_angle=180,
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tilt_angle=tilt_angle,
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standard_meridian=-75)
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solar_angles = solar_parameters.solar_angles # Obtain solar angles for further analysis
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solar_parameters.tilted_irradiance_calculator() # Calculate the solar radiation on a tilted surface
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# Assignation of Energy System Archetypes to Buildings
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#TODO this needs to be modified. We should either use the existing percentages or assign systems based on building
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# functions
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for building in city.buildings:
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building.energy_systems_archetype_name = 'Grid Tied PV System'
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EnergySystemsFactory('montreal_future', city).enrich()
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for building in city.buildings:
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electricity_demand = HourlyElectricityDemand(building).calculate()
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PvSystemAssessment(building=building,
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pv_system=None,
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battery=None,
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electricity_demand=electricity_demand,
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tilt_angle=tilt_angle,
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solar_angles=solar_angles,
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pv_installation_type='rooftop',
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simulation_model_type='explicit',
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module_model_name=None,
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inverter_efficiency=0.95,
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system_catalogue_handler=None,
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roof_percentage_coverage=0.75,
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facade_coverage_percentage=0,
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csv_output=False,
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output_path=pv_assessment_path).enrich()
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print("done")
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