85 lines
4.8 KiB
Python
85 lines
4.8 KiB
Python
import csv
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from pathlib import Path
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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.energy_system_sizing_factory import \
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EnergySystemsSizingFactory
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from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.heat_pump_boiler_tes_heating import \
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HeatPumpBoilerTesHeating
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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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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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# 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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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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EnergySystemsSizingFactory('peak_load_sizing', city).enrich()
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hp = city.buildings[0].energy_systems[1].generation_systems[1]
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boiler = city.buildings[0].energy_systems[1].generation_systems[0]
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tes = city.buildings[0].energy_systems[1].generation_systems[0].energy_storage_systems[0]
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# Step 1: Calculate daily demands
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daily_demands = [sum(city.buildings[0].heating_demand[cte.HOUR][i:i + 24]) for i in
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range(0, len(city.buildings[0].heating_demand[cte.HOUR]), 24)]
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# Step 2: Find the day with maximum demand
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max_day_index = daily_demands.index(max(daily_demands))
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# Step 3: Extract the hourly demands for the day before, the day with max demand, and the day after
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# Ensure you don't go out of bounds if max_day_index is 0 or the last day
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if max_day_index > 0 and max_day_index < len(daily_demands) - 1:
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start_index = (max_day_index - 1) * 24
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end_index = (max_day_index + 2) * 24
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three_day_demands = city.buildings[0].heating_demand[cte.HOUR][start_index:end_index]
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elif max_day_index == 0: # If max is the first day, just include day 1 and day 2
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start_index = max_day_index * 24
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end_index = (max_day_index + 2) * 24
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three_day_demands = city.buildings[0].heating_demand[cte.HOUR][start_index:end_index]
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else: # If max is the last day, just include the last two days
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start_index = (max_day_index - 1) * 24
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end_index = len(city.buildings[0].heating_demand[cte.HOUR])
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three_day_demands = city.buildings[0].heating_demand[cte.HOUR][start_index:end_index]
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results = HeatPumpBoilerTesHeating(hp=hp,
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boiler=boiler,
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tes=tes,
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hourly_heating_demand_joules=three_day_demands,
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heating_peak_load_watts=max(three_day_demands),
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upper_limit_tes=55,
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outdoor_temperature=city.buildings[0].external_temperature[cte.HOUR],
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dt=900).simulation()
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heating_eui = city.buildings[0].heating_demand[cte.YEAR][0] / (
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city.buildings[0].thermal_zones_from_internal_zones[0].total_floor_area * 3.6e6)
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file_name = f'energy_system_simulation_results_{city.buildings[0].name}.csv'
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with open(simulation_results_path / file_name, 'w', newline='') as csvfile:
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output_file = csv.writer(csvfile)
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# Write header
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output_file.writerow(results.keys())
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# Write data
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output_file.writerows(zip(*results.values()))
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