115 lines
6.7 KiB
Python
115 lines
6.7 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 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.pv_assessment.pv_system_assessment import \
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PvSystemAssessment
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from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.solar_calculator import \
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SolarCalculator
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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 energy_system_modelling_package.energy_system_retrofit.energy_system_retrofit_report import EnergySystemRetrofitReport
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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 EnergySystemsSizingFactory
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from energy_system_modelling_package.energy_system_retrofit.energy_system_retrofit_results import consumption_data, cost_data
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from costing_package.cost import Cost
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from costing_package.constants import SYSTEM_RETROFIT_AND_PV, CURRENT_STATUS
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from hub.exports.exports_factory import ExportsFactory
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# Specify the GeoJSON file path
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main_path = Path(__file__).parent.resolve()
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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, path=main_path)
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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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pv_assessment_path = output_path / 'pv_outputs'
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pv_assessment_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_systems_percentage)
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EnergySystemsFactory('montreal_custom', city).enrich()
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EnergySystemsSizingFactory('peak_load_sizing', city).enrich()
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current_status_energy_consumption = consumption_data(city)
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current_status_life_cycle_cost = {}
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for building in city.buildings:
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cost_retrofit_scenario = CURRENT_STATUS
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lcc_dataframe = Cost(building=building,
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retrofit_scenario=cost_retrofit_scenario,
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fuel_tariffs=['Electricity-D', 'Gas-Energir']).life_cycle
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lcc_dataframe.to_csv(cost_analysis_output_path / f'{building.name}_current_status_lcc.csv')
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current_status_life_cycle_cost[f'{building.name}'] = cost_data(building, lcc_dataframe, cost_retrofit_scenario)
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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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EnergySystemsSizingFactory('peak_load_sizing', 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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for building in city.buildings:
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MontrealEnergySystemArchetypesSimulationFactory(f'archetype_cluster_{building.energy_systems_archetype_cluster_id}',
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building,
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simulation_results_path).enrich()
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if 'PV' in building.energy_systems_archetype_name:
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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=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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retrofitted_energy_consumption = consumption_data(city)
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retrofitted_life_cycle_cost = {}
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for building in city.buildings:
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cost_retrofit_scenario = SYSTEM_RETROFIT_AND_PV
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lcc_dataframe = Cost(building=building,
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retrofit_scenario=cost_retrofit_scenario,
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fuel_tariffs=['Electricity-D', 'Gas-Energir']).life_cycle
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lcc_dataframe.to_csv(cost_analysis_output_path / f'{building.name}_retrofitted_lcc.csv')
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retrofitted_life_cycle_cost[f'{building.name}'] = cost_data(building, lcc_dataframe, cost_retrofit_scenario)
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EnergySystemRetrofitReport(city, output_path, 'PV Implementation and System Retrofit',
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current_status_energy_consumption, retrofitted_energy_consumption,
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current_status_life_cycle_cost, retrofitted_life_cycle_cost).create_report()
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