59 lines
3.4 KiB
Python
59 lines
3.4 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 energy_system_modelling_package.energy_system_modelling_factories.montreal_energy_system_archetype_modelling_factory import \
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MontrealEnergySystemArchetypesSimulationFactory
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from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.genetic_algorithm.multi_objective_genetic_algorithm import MultiObjectiveGeneticAlgorithm
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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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import hub.helpers.constants as cte
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from building_modelling.geojson_creator import process_geojson
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from energy_system_modelling_package import random_assignation
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from hub.imports.energy_systems_factory import EnergySystemsFactory
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from energy_system_modelling_package.energy_system_modelling_factories.energy_system_sizing_factory import \
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EnergySystemsSizingFactory
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from energy_system_modelling_package.energy_system_retrofit.energy_system_retrofit_results import consumption_data, \
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cost_data
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from costing_package.cost import Cost
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from costing_package.constants import *
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from hub.exports.exports_factory import ExportsFactory
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# Specify the GeoJSON file path
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input_files_path = (Path(__file__).parent / 'input_files')
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input_files_path.mkdir(parents=True, exist_ok=True)
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geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.00006)
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geojson_file_path = input_files_path / 'output_buildings.geojson'
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output_path = (Path(__file__).parent / 'out_files').resolve()
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output_path.mkdir(parents=True, exist_ok=True)
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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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simulation_results_path = (Path(__file__).parent / 'out_files' / 'simulation_results').resolve()
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simulation_results_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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cost_analysis_output_path = output_path / 'cost_analysis'
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cost_analysis_output_path.mkdir(parents=True, exist_ok=True)
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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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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('sra', city, sra_output_path).export()
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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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ResultFactory('sra', city, sra_output_path).enrich()
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energy_plus_workflow(city, energy_plus_output_path)
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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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design_period_demands = MultiObjectiveGeneticAlgorithm(optimization_scenario='energy-consumption_cost').design_period_identification(city.buildings[0])
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heating_demand = design_period_demands[cte.HEATING]['demands']
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