s_ranjbar
05d88e2461
fix: simulation models and cop curves modified and finalized fix: all system simulation models are fixed
101 lines
5.5 KiB
Python
101 lines
5.5 KiB
Python
from pathlib import Path
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import subprocess
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from scripts.ep_run_enrich import energy_plus_workflow
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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.energy_system_retrofit_report import EnergySystemRetrofitReport
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from scripts.geojson_creator import process_geojson
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from scripts import random_assignation
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from hub.imports.energy_systems_factory import EnergySystemsFactory
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from scripts.energy_system_sizing import SystemSizing
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from scripts.solar_angles import CitySolarAngles
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from scripts.pv_sizing_and_simulation import PVSizingSimulation
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from scripts.energy_system_retrofit_results import consumption_data, cost_data
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from scripts.energy_system_sizing_and_simulation_factory import EnergySystemsSimulationFactory
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from scripts.costs.cost import Cost
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from scripts.costs.constants import SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT_AND_PV, CURRENT_STATUS
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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_feasibility import pv_feasibility
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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=0.0001)
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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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pv_feasibility(-73.5681295982132, 45.49218262677643, 0.0001, selected_buildings=city.buildings)
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energy_plus_workflow(city, energy_plus_output_path)
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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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random_assignation.call_random(city.buildings, random_assignation.residential_systems_percentage)
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EnergySystemsFactory('montreal_custom', city).enrich()
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SystemSizing(city.buildings).montreal_custom()
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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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for building in city.buildings:
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if 'PV' in building.energy_systems_archetype_name:
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ghi = [x / cte.WATTS_HOUR_TO_JULES 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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if building.energy_systems_archetype_name == 'PV+4Pipe+DHW':
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EnergySystemsSimulationFactory('archetype13', building=building, output_path=simulation_results_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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for i in range(12):
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dhw_consumption = 0
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for building in city.buildings:
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dhw_consumption += building.domestic_hot_water_consumption[cte.MONTH][i] / 3.6e6
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