131 lines
5.7 KiB
Python
131 lines
5.7 KiB
Python
"""
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Monthly energy balance main
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SPDX - License - Identifier: LGPL - 3.0 - or -later
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Copyright © 2020 Project Author Pilar Monsalvete Álvarez de Uribarri pilar.monsalvete@concordia.ca
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"""
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import sys
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from pathlib import Path
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from argparse import ArgumentParser
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import ast
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import pandas as pd
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import datetime
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import helpers.constants as cte
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from helpers import monthly_values as mv
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from simplified_radiosity_algorithm import SimplifiedRadiosityAlgorithm
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from imports.geometry_factory import GeometryFactory
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from imports.weather_factory import WeatherFactory
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from imports.construction_factory import ConstructionFactory
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from imports.usage_factory import UsageFactory
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from exports.energy_building_exports_factory import EnergyBuildingsExportsFactory
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from insel.monthly_demand_calculation import MonthlyDemandCalculation
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parser = ArgumentParser(description='Monthly energy balance workflow v1.0.')
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required = parser.add_argument_group('required arguments')
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parser.add_argument('--geometry_type', '-g', help='Geometry type {citygml}', default='citygml')
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required.add_argument('--input_geometry_file', '-i', help='Input geometry file', required=True)
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parser.add_argument('--use_cached_sra_file', '-u', help='Use sra files from cache, instead of freshly calculated sra '
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'files', default=False)
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required.add_argument('--project_folder', '-f', help='Project folder', required=True)
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required.add_argument('--weather_file_name', '-w', help='Weather file', required=True)
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required.add_argument('--climate_reference_city', '-c', help='Closest city with climate weather', required=True)
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try:
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args = parser.parse_args()
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except SystemExit:
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sys.exit()
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keep_files = True
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print('begin_time', datetime.datetime.now())
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# Step 1: Initialize the city model
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file = Path(args.input_geometry_file).resolve()
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city = GeometryFactory(args.geometry_type, file).city
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for building in city.buildings:
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volume = building.volume
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if str(volume) == 'inf':
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sys.stderr.write(f'Building {building.name} has geometry errors. It has been removed from the city\n')
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city.remove_city_object(building)
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print('begin_populating_time', datetime.datetime.now())
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# Step 2: Populate city adding thermal- and usage-related parameters
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for building in city.buildings:
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building.year_of_construction = 2006
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if building.function is None:
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building.function = 'large office'
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building.attic_heated = 0
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building.basement_heated = 1
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ConstructionFactory('nrel', city).enrich()
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UsageFactory('comnet', city).enrich()
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print('begin_weather_time', datetime.datetime.now())
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# Step 3: Populate city adding climate-related parameters
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weather_format = 'epw'
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city.climate_reference_city = args.climate_reference_city
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tmp_path = (Path(args.project_folder) / 'tmp').resolve()
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city.climate_file = (tmp_path / f'{args.climate_reference_city}.cli').resolve()
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WeatherFactory(weather_format, city, file_name=args.weather_file_name).enrich()
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for building in city.buildings:
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if cte.HOUR not in building.external_temperature:
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print('No external temperature found')
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sys.exit()
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if cte.MONTH not in building.external_temperature:
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building.external_temperature[cte.MONTH] = mv.MonthlyValues().\
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get_mean_values(building.external_temperature[cte.HOUR][[weather_format]])
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max_buildings_handled_by_sra = 500
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sra = SimplifiedRadiosityAlgorithm(city, Path(args.project_folder).resolve(), args.weather_file_name)
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if ast.literal_eval(args.use_cached_sra_file):
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sra.set_irradiance_surfaces(city)
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else:
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total_number_of_buildings = len(city.buildings)
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if total_number_of_buildings > max_buildings_handled_by_sra:
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radius = 80
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for building in city.buildings:
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new_city = city.region(building.centroid, radius)
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sra_new = SimplifiedRadiosityAlgorithm(new_city, Path(args.project_folder).resolve(), args.weather_file_name)
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sra_new.call_sra(weather_format, keep_files=True)
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sra_new.set_irradiance_surfaces(city, building_name=building.name)
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else:
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sra.call_sra(weather_format, keep_files=keep_files)
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sra.set_irradiance_surfaces(city)
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print('begin_insel_time', datetime.datetime.now())
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# Step 5: Demand calculation calling INSEL
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EnergyBuildingsExportsFactory('insel_monthly_energy_balance', city, tmp_path).export()
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insel = MonthlyDemandCalculation(city, tmp_path, weather_format)
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insel.run()
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insel.results()
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print('begin_write_results_time', datetime.datetime.now())
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# Step 6: Print results
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print_results = None
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file = 'city name: ' + city.name + '\n'
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for building in city.buildings:
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insel_file_name = building.name + '.insel'
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heating_results = building.heating[cte.MONTH].rename(columns={'INSEL': f'{building.name} heating Wh'})
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cooling_results = building.cooling[cte.MONTH].rename(columns={'INSEL': f'{building.name} cooling Wh'})
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if print_results is None:
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print_results = heating_results
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else:
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print_results = pd.concat([print_results, heating_results], axis='columns')
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print_results = pd.concat([print_results, cooling_results], axis='columns')
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file += '\n'
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file += 'name: ' + building.name + '\n'
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file += 'year of construction: ' + str(building.year_of_construction) + '\n'
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file += 'function: ' + building.function + '\n'
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file += 'floor area: ' + str(building.internal_zones[0].area) + '\n'
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file += 'storeys: ' + str(building.storeys_above_ground) + '\n'
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file += 'heated_volume: ' + str(building.volume) + '\n'
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file += 'volume: ' + str(building.volume) + '\n'
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full_path_results = Path(args.project_folder + '/outputs/demand.csv').resolve()
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print_results.to_csv(full_path_results)
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full_path_metadata = Path(args.project_folder + '/outputs/metadata.csv').resolve()
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with open(full_path_metadata, 'w') as metadata_file:
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metadata_file.write(file)
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print('end_time', datetime.datetime.now())
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