167 lines
7.3 KiB
Python
167 lines
7.3 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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from helpers import monthly_values as mv
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from populate import Populate
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from insel.insel import Insel
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from insel.templates.monthly_energy_balance import MonthlyEnergyBalance as templates
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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 city_model_structure.city import City
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import datetime
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parser = ArgumentParser(description='Monthly energy balance workflow v0.1.')
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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_pickle_file', '-p', help='Use pickle file instead of importing geometry file', default=False,
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required=True)
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parser.add_argument('--populated_step_in_pickle', '-ps', help='Physics and usage parameters already in pickle file',
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default=False, required=True)
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parser.add_argument('--weather_step_in_pickle', '-ws', help='Weather parameters already in pickle file',
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default=False, 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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pickle_file = ''
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if ast.literal_eval(args.use_pickle_file):
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pickle_file = Path(args.input_geometry_file).resolve()
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city = City.load(pickle_file)
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else:
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file = Path(args.input_geometry_file).resolve()
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pickle_file = Path(str(file).replace('.gml', '.pickle'))
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city = GeometryFactory(args.geometry_type, file).city
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city.save(pickle_file)
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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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populated_step_in_pickle = ast.literal_eval(args.populated_step_in_pickle)
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if populated_step_in_pickle:
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populated_city = city
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else:
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populated_city = Populate(city).populated_city
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pickle_file = Path(str(pickle_file).replace('.pickle', '_populated.pickle'))
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populated_city.save(pickle_file)
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if populated_city.buildings is None:
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print('No building to be calculated')
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sys.exit()
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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_step_in_pickle = ast.literal_eval(args.weather_step_in_pickle)
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if not weather_step_in_pickle:
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city.climate_reference_city = args.climate_reference_city
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path = (Path(args.project_folder) / 'tmp').resolve()
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city.climate_file = (path / f'{args.climate_reference_city}.cli').resolve()
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WeatherFactory('epw', populated_city, file_name=args.weather_file_name).enrich()
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for building in populated_city.buildings:
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if '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 'month' not in building.external_temperature:
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building.external_temperature['month'] = mv.MonthlyValues().\
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get_mean_values(building.external_temperature['hour'][['epw']])
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max_buildings_handled_by_sra = 500
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for building in city.buildings:
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for surface in building.surfaces:
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surface.swr = 0.2
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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.results()
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sra.set_irradiance_surfaces(populated_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 = 100
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for building in city.buildings:
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new_city = city.region(building.location, radius)
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sra_new = SimplifiedRadiosityAlgorithm(city, Path(args.project_folder).resolve(), args.weather_file_name)
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sra_new.call_sra(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(keep_files=keep_files)
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sra.set_irradiance_surfaces(populated_city)
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pickle_file = Path(str(pickle_file).replace('.pickle', '_weather.pickle'))
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populated_city.save(pickle_file)
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print('begin_insel_time', datetime.datetime.now())
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# Step 4: Demand calculation (one model per building)
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print_results = None
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file = 'city name: ' + city.name + '\n'
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for building in populated_city.buildings:
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if building.name != 'BLD122177':
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# todo: default values to be defined at each specific workflow!
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building.heated = True
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building.cooled = False
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building.attic_heated = 1
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building.basement_heated = 1
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building.usage_zones[0].internal_gains[0].average_internal_gain = 10.45
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full_path_out = Path(args.project_folder + '/outputs/' + building.name + '_insel.out').resolve()
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full_path_out.parent.mkdir(parents=True, exist_ok=True)
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insel_file_name = building.name + '.insel'
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try:
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key = 'epw'
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content = templates.generate_meb_template(building, full_path_out, key)
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insel = Insel(Path(args.project_folder).resolve(), insel_file_name, content, mode=2, keep_files=keep_files).run()
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building.heating['month'], building.cooling['month'] = templates.demand(full_path_out)
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new_building = building.heating['month'].rename(columns={'INSEL': building.name})
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demand_year = 0
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for value in building.heating['month']['INSEL']:
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if value == 'NaN':
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value = '0'
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demand_year += float(value)
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yearly_heating = pd.DataFrame([demand_year], columns=['INSEL'])
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building.heating['year'] = yearly_heating
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if print_results is None:
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print_results = new_building
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else:
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print_results = pd.concat([print_results, new_building], 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: ' + building.year_of_construction + '\n'
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file += 'function: ' + building.function + '\n'
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file += 'floor area: ' + str(building.thermal_zones[0].floor_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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except ValueError:
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print(sys.exc_info()[1])
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print('Building ' + building.name + ' could not be processed')
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continue
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full_path_results = Path(args.project_folder + '/outputs/heating_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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pickle_file = Path(str(pickle_file).replace('.pickle', '_demand.pickle'))
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populated_city.save(pickle_file)
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print('end_time', datetime.datetime.now())
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