adapted to no dataframes results
This commit is contained in:
parent
5cb01fc74c
commit
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11
main.py
11
main.py
@ -12,10 +12,9 @@ from sra_engine import SraEngine
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try:
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try:
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file_path = (Path(__file__).parent / 'input_files' / 'eilat.geojson')
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file_path = (Path(__file__).parent / 'input_files' / '228730.geojson')
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climate_reference_city = 'Montreal'
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construction_format = 'nrcan'
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construction_format = 'nrcan'
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usage_format = 'eilat'
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usage_format = 'nrcan'
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energy_systems_format = 'montreal_custom'
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energy_systems_format = 'montreal_custom'
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out_path = (Path(__file__).parent / 'output_files')
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out_path = (Path(__file__).parent / 'output_files')
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@ -27,7 +26,7 @@ try:
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height_field='heightmax',
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height_field='heightmax',
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year_of_construction_field='ANNEE_CONS',
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year_of_construction_field='ANNEE_CONS',
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function_field='CODE_UTILI',
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function_field='CODE_UTILI',
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function_to_hub=Dictionaries().eilat_function_to_hub_function).city
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function_to_hub=Dictionaries().montreal_function_to_hub_function).city
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print(f'city created from {file_path}')
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print(f'city created from {file_path}')
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ConstructionFactory(construction_format, city).enrich()
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ConstructionFactory(construction_format, city).enrich()
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@ -40,10 +39,8 @@ try:
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print('enrich systems... done')
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print('enrich systems... done')
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print('exporting:')
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print('exporting:')
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sra_file = (tmp_folder / f'{city.name}_sra.xml').resolve()
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SraEngine(city, tmp_folder)
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SraEngine(city, sra_file, tmp_folder)
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print(' sra processed...')
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print(' sra processed...')
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MonthlyEnergyBalanceEngine(city, tmp_folder)
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MonthlyEnergyBalanceEngine(city, tmp_folder)
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print(' insel processed...')
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print(' insel processed...')
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295
results.py
295
results.py
@ -9,68 +9,50 @@ class Results:
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self._path = path
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self._path = path
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def print(self):
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def print(self):
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print_results = None
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file = 'city name: ' + self._city.name + '\n'
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file = 'city name: ' + self._city.name + '\n'
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array = [None] * 12
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for building in self._city.buildings:
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for building in self._city.buildings:
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if cte.MONTH in building.heating_demand.keys():
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if cte.MONTH in building.heating_demand.keys():
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heating_results = building.heating_demand[cte.MONTH].rename(columns={cte.INSEL_MEB: f'{building.name} heating Wh'})
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heating_results = building.heating_demand[cte.MONTH]
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else:
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else:
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heating_results = pd.DataFrame(array, columns=[f'{building.name} heating demand Wh'])
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heating_results = [None] * 12
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if cte.MONTH in building.cooling_demand.keys():
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if cte.MONTH in building.cooling_demand.keys():
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cooling_results = building.cooling_demand[cte.MONTH].rename(columns={cte.INSEL_MEB: f'{building.name} cooling Wh'})
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cooling_results = building.cooling_demand[cte.MONTH]
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else:
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else:
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cooling_results = pd.DataFrame(array, columns=[f'{building.name} cooling demand Wh'])
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cooling_results = [None] * 12
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if cte.MONTH in building.lighting_electrical_demand.keys():
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if cte.MONTH in building.lighting_electrical_demand.keys():
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lighting_results = building.lighting_electrical_demand[cte.MONTH]\
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lighting_results = building.lighting_electrical_demand[cte.MONTH]
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.rename(columns={cte.INSEL_MEB: f'{building.name} lighting electrical demand Wh'})
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else:
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else:
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lighting_results = pd.DataFrame(array, columns=[f'{building.name} lighting electrical demand Wh'])
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lighting_results = [None] * 12
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if cte.MONTH in building.appliances_electrical_demand.keys():
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if cte.MONTH in building.appliances_electrical_demand.keys():
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appliances_results = building.appliances_electrical_demand[cte.MONTH]\
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appliances_results = building.appliances_electrical_demand[cte.MONTH]
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.rename(columns={cte.INSEL_MEB: f'{building.name} appliances electrical demand Wh'})
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else:
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else:
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appliances_results = pd.DataFrame(array, columns=[f'{building.name} appliances electrical demand Wh'])
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appliances_results = [None] * 12
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if cte.MONTH in building.domestic_hot_water_heat_demand.keys():
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if cte.MONTH in building.domestic_hot_water_heat_demand.keys():
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dhw_results = building.domestic_hot_water_heat_demand[cte.MONTH]\
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dhw_results = building.domestic_hot_water_heat_demand[cte.MONTH]
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.rename(columns={cte.INSEL_MEB: f'{building.name} domestic hot water demand Wh'})
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else:
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else:
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dhw_results = pd.DataFrame(array, columns=[f'{building.name} domestic hot water demand Wh'])
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dhw_results = [None] * 12
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if cte.MONTH in building.heating_consumption.keys():
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if cte.MONTH in building.heating_consumption.keys():
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heating_consumption_results = pd.DataFrame(building.heating_consumption[cte.MONTH],
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heating_consumption_results = building.heating_consumption[cte.MONTH]
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columns=[f'{building.name} heating consumption Wh'])
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else:
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else:
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heating_consumption_results = pd.DataFrame(array, columns=[f'{building.name} heating consumption Wh'])
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heating_consumption_results = [None] * 12
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if cte.MONTH in building.cooling_consumption.keys():
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if cte.MONTH in building.cooling_consumption.keys():
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cooling_consumption_results = pd.DataFrame(building.cooling_consumption[cte.MONTH],
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cooling_consumption_results = building.cooling_consumption[cte.MONTH]
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columns=[f'{building.name} cooling consumption Wh'])
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else:
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else:
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cooling_consumption_results = pd.DataFrame(array, columns=[f'{building.name} cooling consumption Wh'])
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cooling_consumption_results = [None] * 12
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if cte.MONTH in building.domestic_hot_water_consumption.keys():
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if cte.MONTH in building.domestic_hot_water_consumption.keys():
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dhw_consumption_results = pd.DataFrame(building.domestic_hot_water_consumption[cte.MONTH],
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dhw_consumption_results = building.domestic_hot_water_consumption[cte.MONTH]
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columns=[f'{building.name} domestic hot water consumption Wh'])
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else:
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else:
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dhw_consumption_results = pd.DataFrame(array, columns=[f'{building.name} domestic hot water consumption Wh'])
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dhw_consumption_results = [None] * 12
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if cte.MONTH in building.heating_peak_load.keys():
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if cte.MONTH in building.heating_peak_load.keys():
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heating_peak_load_results = pd.DataFrame(building.heating_peak_load[cte.MONTH],
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heating_peak_load_results = building.heating_peak_load[cte.MONTH]
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columns=[f'{building.name} heating peak load W'])
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else:
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else:
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heating_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} heating peak load W'])
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heating_peak_load_results = [None] * 12
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if cte.MONTH in building.cooling_peak_load.keys():
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if cte.MONTH in building.cooling_peak_load.keys():
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cooling_peak_load_results = pd.DataFrame(building.cooling_peak_load[cte.MONTH],
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cooling_peak_load_results = building.cooling_peak_load[cte.MONTH]
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columns=[f'{building.name} cooling peak load W'])
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else:
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else:
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cooling_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} cooling peak load W'])
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cooling_peak_load_results = [None] * 12
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if cte.MONTH in building.onsite_electrical_production.keys():
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monthly_onsite_electrical_production = building.onsite_electrical_production[cte.MONTH]
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onsite_electrical_production = pd.DataFrame(monthly_onsite_electrical_production,
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columns=[f'{building.name} onsite electrical production Wh'])
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else:
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onsite_electrical_production = pd.DataFrame(array, columns=[f'{building.name} onsite electrical production Wh'])
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heating = 0
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heating = 0
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cooling = 0
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cooling = 0
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for system in building.energy_systems:
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for system in building.energy_systems:
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@ -82,17 +64,17 @@ class Results:
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if cte.MONTH in building.heating_peak_load.keys() and cte.MONTH in building.cooling_peak_load.keys():
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if cte.MONTH in building.heating_peak_load.keys() and cte.MONTH in building.cooling_peak_load.keys():
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peak_lighting = 0
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peak_lighting = 0
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peak_appliances = 0
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peak_appliances = 0
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for thermal_zone in building.internal_zones[0].thermal_zones:
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thermal_zone = building.thermal_zones_from_internal_zones[0]
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lighting = thermal_zone.lighting
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lighting = thermal_zone.lighting
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for schedule in lighting.schedules:
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for schedule in lighting.schedules:
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for value in schedule.values:
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for value in schedule.values:
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if value * lighting.density * thermal_zone.total_floor_area > peak_lighting:
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if value * lighting.density * thermal_zone.total_floor_area > peak_lighting:
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peak_lighting = value * lighting.density * thermal_zone.total_floor_area
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peak_lighting = value * lighting.density * thermal_zone.total_floor_area
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appliances = thermal_zone.appliances
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appliances = thermal_zone.appliances
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for schedule in appliances.schedules:
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for schedule in appliances.schedules:
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for value in schedule.values:
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for value in schedule.values:
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if value * appliances.density * thermal_zone.total_floor_area > peak_appliances:
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if value * appliances.density * thermal_zone.total_floor_area > peak_appliances:
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peak_appliances = value * appliances.density * thermal_zone.total_floor_area
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peak_appliances = value * appliances.density * thermal_zone.total_floor_area
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monthly_electricity_peak = [0.9 * peak_lighting + 0.7 * peak_appliances] * 12
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monthly_electricity_peak = [0.9 * peak_lighting + 0.7 * peak_appliances] * 12
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conditioning_peak = []
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conditioning_peak = []
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@ -103,170 +85,36 @@ class Results:
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conditioning_peak.append(heating * value)
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conditioning_peak.append(heating * value)
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monthly_electricity_peak[i] += 0.8 * conditioning_peak[i]
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monthly_electricity_peak[i] += 0.8 * conditioning_peak[i]
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electricity_peak_load_results = pd.DataFrame(monthly_electricity_peak
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electricity_peak_load_results = monthly_electricity_peak
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, columns=[f'{building.name} electricity peak load W'])
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else:
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else:
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electricity_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} electricity peak load W'])
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electricity_peak_load_results = [None] * 12
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if cte.MONTH in building.onsite_electrical_production.keys():
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monthly_onsite_electrical_production = building.onsite_electrical_production[cte.MONTH]
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onsite_electrical_production = monthly_onsite_electrical_production
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else:
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onsite_electrical_production = [None] * 12
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if cte.MONTH in building.distribution_systems_electrical_consumption.keys():
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if cte.MONTH in building.distribution_systems_electrical_consumption.keys():
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extra_electrical_consumption = pd.DataFrame(building.distribution_systems_electrical_consumption[cte.MONTH],
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extra_electrical_consumption = building.distribution_systems_electrical_consumption[cte.MONTH]
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columns=[
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f'{building.name} electrical consumption for distribution Wh'])
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else:
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else:
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extra_electrical_consumption = pd.DataFrame(array,
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extra_electrical_consumption = [None] * 12
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columns=[
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f'{building.name} electrical consumption for distribution Wh'])
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if print_results is None:
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columns_names = [f'{building.name} heating demand J',
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print_results = heating_results
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f'{building.name} cooling demand J',
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else:
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f'{building.name} lighting demand J',
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print_results = pd.concat([print_results, heating_results], axis='columns')
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f'{building.name} appliances demand J',
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print_results = pd.concat([print_results,
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f'{building.name} domestic hot water demand J',
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cooling_results,
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f'{building.name} heating consumption J',
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lighting_results,
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f'{building.name} cooling consumption J',
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appliances_results,
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f'{building.name} domestic hot water consumption J',
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dhw_results,
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f'{building.name} heating peak load W',
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heating_consumption_results,
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f'{building.name} cooling peak load W',
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cooling_consumption_results,
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f'{building.name} electricity peak load W',
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dhw_consumption_results,
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f'{building.name} onsite electrical production J',
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heating_peak_load_results,
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f'{building.name} extra electrical consumption J'
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cooling_peak_load_results,
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]
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electricity_peak_load_results,
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print_results = pd.DataFrame([heating_results,
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onsite_electrical_production,
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extra_electrical_consumption], axis='columns')
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file += '\n'
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file += f'name: {building.name}\n'
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file += f'year of construction: {building.year_of_construction}\n'
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file += f'function: {building.function}\n'
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file += f'floor area: {building.floor_area}\n'
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if building.average_storey_height is not None and building.eave_height is not None:
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file += f'storeys: {int(building.eave_height / building.average_storey_height)}\n'
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else:
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file += f'storeys: n/a\n'
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file += f'volume: {building.volume}\n'
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full_path_results = Path(self._path / 'demand.csv').resolve()
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print_results.to_csv(full_path_results, na_rep='null')
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full_path_metadata = Path(self._path / '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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def outputsforgraph(self):
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file = 'city name: ' + self._city.name + '\n'
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array = [None] * 12
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for building in self._city.buildings:
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if cte.MONTH in building.heating_demand.keys():
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heating_results = building.heating_demand[cte.MONTH].rename(
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columns={cte.INSEL_MEB: f'{building.name} heating Wh'})
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else:
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heating_results = pd.DataFrame(array, columns=[f'{building.name} heating demand Wh'])
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if cte.MONTH in building.cooling_demand.keys():
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cooling_results = building.cooling_demand[cte.MONTH].rename(
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columns={cte.INSEL_MEB: f'{building.name} cooling Wh'})
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else:
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cooling_results = pd.DataFrame(array, columns=[f'{building.name} cooling demand Wh'])
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if cte.MONTH in building.lighting_electrical_demand.keys():
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lighting_results = building.lighting_electrical_demand[cte.MONTH] \
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.rename(columns={cte.INSEL_MEB: f'{building.name} lighting electrical demand Wh'})
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else:
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lighting_results = pd.DataFrame(array, columns=[f'{building.name} lighting electrical demand Wh'])
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if cte.MONTH in building.appliances_electrical_demand.keys():
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appliances_results = building.appliances_electrical_demand[cte.MONTH] \
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.rename(columns={cte.INSEL_MEB: f'{building.name} appliances electrical demand Wh'})
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else:
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appliances_results = pd.DataFrame(array, columns=[f'{building.name} appliances electrical demand Wh'])
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if cte.MONTH in building.domestic_hot_water_heat_demand.keys():
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dhw_results = building.domestic_hot_water_heat_demand[cte.MONTH] \
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.rename(columns={cte.INSEL_MEB: f'{building.name} domestic hot water demand Wh'})
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else:
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dhw_results = pd.DataFrame(array, columns=[f'{building.name} domestic hot water demand Wh'])
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if cte.MONTH in building.heating_consumption.keys():
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heating_consumption_results = pd.DataFrame(building.heating_consumption[cte.MONTH],
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columns=[f'{building.name} heating consumption Wh'])
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else:
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heating_consumption_results = pd.DataFrame(array, columns=[f'{building.name} heating consumption Wh'])
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if cte.MONTH in building.cooling_consumption.keys():
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cooling_consumption_results = pd.DataFrame(building.cooling_consumption[cte.MONTH],
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columns=[f'{building.name} cooling consumption Wh'])
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else:
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cooling_consumption_results = pd.DataFrame(array, columns=[f'{building.name} cooling consumption Wh'])
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if cte.MONTH in building.domestic_hot_water_consumption.keys():
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dhw_consumption_results = pd.DataFrame(building.domestic_hot_water_consumption[cte.MONTH],
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columns=[f'{building.name} domestic hot water consumption Wh'])
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else:
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dhw_consumption_results = pd.DataFrame(array, columns=[f'{building.name} domestic hot water consumption Wh'])
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if cte.MONTH in building.heating_peak_load.keys():
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heating_peak_load_results = pd.DataFrame(building.heating_peak_load[cte.MONTH],
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columns=[f'{building.name} heating peak load W'])
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else:
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heating_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} heating peak load W'])
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if cte.MONTH in building.cooling_peak_load.keys():
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cooling_peak_load_results = pd.DataFrame(building.cooling_peak_load[cte.MONTH],
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columns=[f'{building.name} cooling peak load W'])
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else:
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cooling_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} cooling peak load W'])
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if cte.MONTH in building.onsite_electrical_production.keys():
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monthly_onsite_electrical_production = building.onsite_electrical_production[cte.MONTH]
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onsite_electrical_production = pd.DataFrame(monthly_onsite_electrical_production,
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columns=[f'{building.name} onsite electrical production Wh'])
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else:
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onsite_electrical_production = pd.DataFrame(array,
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columns=[f'{building.name} onsite electrical production Wh'])
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heating = 0
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cooling = 0
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for system in building.energy_systems:
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for demand_type in system.demand_types:
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if demand_type == cte.HEATING:
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heating = 1
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if demand_type == cte.COOLING:
|
|
||||||
cooling = 1
|
|
||||||
if cte.MONTH in building.heating_peak_load.keys() and cte.MONTH in building.cooling_peak_load.keys():
|
|
||||||
peak_lighting = 0
|
|
||||||
peak_appliances = 0
|
|
||||||
for thermal_zone in building.internal_zones[0].thermal_zones:
|
|
||||||
lighting = thermal_zone.lighting
|
|
||||||
for schedule in lighting.schedules:
|
|
||||||
for value in schedule.values:
|
|
||||||
if value * lighting.density * thermal_zone.total_floor_area > peak_lighting:
|
|
||||||
peak_lighting = value * lighting.density * thermal_zone.total_floor_area
|
|
||||||
appliances = thermal_zone.appliances
|
|
||||||
for schedule in appliances.schedules:
|
|
||||||
for value in schedule.values:
|
|
||||||
if value * appliances.density * thermal_zone.total_floor_area > peak_appliances:
|
|
||||||
peak_appliances = value * appliances.density * thermal_zone.total_floor_area
|
|
||||||
|
|
||||||
monthly_electricity_peak = [0.9 * peak_lighting + 0.7 * peak_appliances] * 12
|
|
||||||
conditioning_peak = []
|
|
||||||
for i, value in enumerate(building.heating_peak_load[cte.MONTH]):
|
|
||||||
if cooling * building.cooling_peak_load[cte.MONTH][i] > heating * value:
|
|
||||||
conditioning_peak.append(cooling * building.cooling_peak_load[cte.MONTH][i])
|
|
||||||
else:
|
|
||||||
conditioning_peak.append(heating * value)
|
|
||||||
monthly_electricity_peak[i] += 0.8 * conditioning_peak[i]
|
|
||||||
|
|
||||||
electricity_peak_load_results = pd.DataFrame(monthly_electricity_peak
|
|
||||||
, columns=[f'{building.name} electricity peak load W'])
|
|
||||||
else:
|
|
||||||
electricity_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} electricity peak load W'])
|
|
||||||
|
|
||||||
if cte.MONTH in building.distribution_systems_electrical_consumption.keys():
|
|
||||||
extra_electrical_consumption = pd.DataFrame(building.distribution_systems_electrical_consumption[cte.MONTH],
|
|
||||||
columns=[
|
|
||||||
f'{building.name} electrical consumption for distribution Wh'])
|
|
||||||
else:
|
|
||||||
extra_electrical_consumption = pd.DataFrame(array,
|
|
||||||
columns=[
|
|
||||||
f'{building.name} electrical consumption for distribution Wh'])
|
|
||||||
|
|
||||||
if print_results is None:
|
|
||||||
print_results = heating_results
|
|
||||||
else:
|
|
||||||
print_results = pd.concat([print_results, heating_results], axis='columns')
|
|
||||||
print_results = pd.concat([print_results,
|
|
||||||
cooling_results,
|
cooling_results,
|
||||||
lighting_results,
|
lighting_results,
|
||||||
appliances_results,
|
appliances_results,
|
||||||
@ -278,20 +126,21 @@ class Results:
|
|||||||
cooling_peak_load_results,
|
cooling_peak_load_results,
|
||||||
electricity_peak_load_results,
|
electricity_peak_load_results,
|
||||||
onsite_electrical_production,
|
onsite_electrical_production,
|
||||||
extra_electrical_consumption], axis='columns')
|
extra_electrical_consumption]).T
|
||||||
file += '\n'
|
print_results.columns = columns_names
|
||||||
file += f'name: {building.name}\n'
|
file += '\n'
|
||||||
file += f'year of construction: {building.year_of_construction}\n'
|
file += f'name: {building.name}\n'
|
||||||
file += f'function: {building.function}\n'
|
file += f'year of construction: {building.year_of_construction}\n'
|
||||||
file += f'floor area: {building.floor_area}\n'
|
file += f'function: {building.function}\n'
|
||||||
if building.average_storey_height is not None and building.eave_height is not None:
|
file += f'floor area: {building.floor_area}\n'
|
||||||
file += f'storeys: {int(building.eave_height / building.average_storey_height)}\n'
|
if building.average_storey_height is not None and building.eave_height is not None:
|
||||||
else:
|
file += f'storeys: {int(building.eave_height / building.average_storey_height)}\n'
|
||||||
file += f'storeys: n/a\n'
|
else:
|
||||||
file += f'volume: {building.volume}\n'
|
file += f'storeys: n/a\n'
|
||||||
|
file += f'volume: {building.volume}\n'
|
||||||
|
|
||||||
full_path_results = Path(self._path / 'demand.csv').resolve()
|
full_path_results = Path(self._path / f'demand_{building.name}.csv').resolve()
|
||||||
print_results.to_csv(full_path_results, na_rep='null')
|
print_results.to_csv(full_path_results, na_rep='null')
|
||||||
full_path_metadata = Path(self._path / 'metadata.csv').resolve()
|
full_path_metadata = Path(self._path / 'metadata.csv').resolve()
|
||||||
with open(full_path_metadata, 'w') as metadata_file:
|
with open(full_path_metadata, 'w') as metadata_file:
|
||||||
metadata_file.write(file)
|
metadata_file.write(file)
|
||||||
|
|||||||
@ -7,14 +7,13 @@ from hub.imports.results_factory import ResultFactory
|
|||||||
|
|
||||||
|
|
||||||
class SraEngine:
|
class SraEngine:
|
||||||
def __init__(self, city, file_path, output_file_path):
|
def __init__(self, city, output_file_path):
|
||||||
"""
|
"""
|
||||||
SRA class
|
SRA class
|
||||||
:param file_path: _sra.xml file path
|
:param city: City
|
||||||
:param output_file_path: path to output the sra calculation
|
:param output_file_path: path to output the sra calculation
|
||||||
"""
|
"""
|
||||||
self._city = city
|
self._city = city
|
||||||
self._file_path = file_path
|
|
||||||
self._output_file_path = output_file_path
|
self._output_file_path = output_file_path
|
||||||
if platform.system() == 'Linux':
|
if platform.system() == 'Linux':
|
||||||
self._executable = 'sra'
|
self._executable = 'sra'
|
||||||
@ -29,6 +28,8 @@ class SraEngine:
|
|||||||
Calls the software
|
Calls the software
|
||||||
"""
|
"""
|
||||||
try:
|
try:
|
||||||
subprocess.run([self._executable, str(self._file_path)], stdout=subprocess.DEVNULL)
|
subprocess.run([self._executable,
|
||||||
|
(self._output_file_path / f'{self._city.name}_sra.xml')],
|
||||||
|
stdout=subprocess.DEVNULL)
|
||||||
except (SubprocessError, TimeoutExpired, CalledProcessError) as error:
|
except (SubprocessError, TimeoutExpired, CalledProcessError) as error:
|
||||||
raise Exception(error)
|
raise Exception(error)
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user