84 lines
4.7 KiB
Python
84 lines
4.7 KiB
Python
from pathlib import Path
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import subprocess
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from building_modelling.ep_run_enrich import energy_plus_workflow
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from building_modelling import random_assignation
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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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from hub.imports.energy_systems_factory import EnergySystemsFactory
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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 building_modelling.geojson_creator import process_geojson
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from hub.exports.exports_factory import ExportsFactory
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# Define paths for input and output directories, ensuring directories are created if they do not exist
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main_path = Path(__file__).parent.parent.resolve()
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input_files_path = (Path(__file__).parent.parent / 'input_files')
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input_files_path.mkdir(parents=True, exist_ok=True)
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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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# Define specific paths for outputs from EnergyPlus and SRA (Simplified Radiosity Algorith) and PV calculation processes
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energy_plus_output_path = output_path / 'energy_plus_outputs'
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energy_plus_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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# Generate a GeoJSON file for city buildings based on latitude, longitude, and building dimensions
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geojson_file_path = input_files_path / 'output_buildings.geojson'
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# Initialize a city object from the geojson file, mapping building functions using a predefined dictionary
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city = GeometryFactory(file_type='geojson',
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path=geojson_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 with construction, usage, and weather information specific to the location
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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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# Execute the EnergyPlus workflow to simulate building energy performance and generate output
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energy_plus_workflow(city, energy_plus_output_path)
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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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# Assign PV system archetype name to the buildings in city
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random_assignation.call_random(city.buildings, random_assignation.residential_systems_percentage)
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# Enrich city model with Montreal future systems parameters
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EnergySystemsFactory('montreal_future', city).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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# # PV modelling building by building
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#List of available PV modules ['RE400CAA Pure 2', 'RE410CAA Pure 2', 'RE420CAA Pure 2', 'RE430CAA Pure 2',
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# 'REC600AA Pro M', 'REC610AA Pro M', 'REC620AA Pro M', 'REC630AA Pro M', 'REC640AA Pro M']
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for building in city.buildings:
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PvSystemAssessment(building=building,
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pv_system=None,
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battery=None,
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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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