147 lines
6.8 KiB
Python
147 lines
6.8 KiB
Python
from pathlib import Path
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import pandas as pd
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import hub.helpers.constants as cte
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class Results:
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def __init__(self, city, path):
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self._city = city
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self._path = path
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def print(self):
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file = 'city name: ' + self._city.name + '\n'
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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]
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else:
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heating_results = [None] * 12
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if cte.MONTH in building.cooling_demand.keys():
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cooling_results = building.cooling_demand[cte.MONTH]
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else:
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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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lighting_results = building.lighting_electrical_demand[cte.MONTH]
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else:
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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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appliances_results = building.appliances_electrical_demand[cte.MONTH]
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else:
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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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dhw_results = building.domestic_hot_water_heat_demand[cte.MONTH]
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else:
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dhw_results = [None] * 12
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if cte.MONTH in building.heating_consumption.keys():
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heating_consumption_results = building.heating_consumption[cte.MONTH]
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else:
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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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cooling_consumption_results = building.cooling_consumption[cte.MONTH]
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else:
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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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dhw_consumption_results = building.domestic_hot_water_consumption[cte.MONTH]
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else:
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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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heating_peak_load_results = building.heating_peak_load[cte.MONTH]
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else:
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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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cooling_peak_load_results = building.cooling_peak_load[cte.MONTH]
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else:
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cooling_peak_load_results = [None] * 12
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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:
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cooling = 1
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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_appliances = 0
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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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for schedule in lighting.schedules:
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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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peak_lighting = value * lighting.density * thermal_zone.total_floor_area
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appliances = thermal_zone.appliances
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for schedule in appliances.schedules:
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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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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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conditioning_peak = []
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for i, value in enumerate(building.heating_peak_load[cte.MONTH]):
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if cooling * building.cooling_peak_load[cte.MONTH][i] > heating * value:
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conditioning_peak.append(cooling * building.cooling_peak_load[cte.MONTH][i])
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else:
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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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electricity_peak_load_results = monthly_electricity_peak
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else:
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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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extra_electrical_consumption = building.distribution_systems_electrical_consumption[cte.MONTH]
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else:
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extra_electrical_consumption = [None] * 12
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columns_names = [f'{building.name} heating demand J',
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f'{building.name} cooling demand J',
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f'{building.name} lighting demand J',
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f'{building.name} appliances demand J',
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f'{building.name} domestic hot water demand J',
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f'{building.name} heating consumption J',
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f'{building.name} cooling consumption J',
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f'{building.name} domestic hot water consumption J',
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f'{building.name} heating peak load W',
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f'{building.name} cooling peak load W',
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f'{building.name} electricity peak load W',
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f'{building.name} onsite electrical production J',
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f'{building.name} extra electrical consumption J'
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]
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print_results = pd.DataFrame([heating_results,
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cooling_results,
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lighting_results,
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appliances_results,
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dhw_results,
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heating_consumption_results,
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cooling_consumption_results,
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dhw_consumption_results,
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heating_peak_load_results,
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cooling_peak_load_results,
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electricity_peak_load_results,
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onsite_electrical_production,
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extra_electrical_consumption]).T
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print_results.columns = columns_names
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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 / f'demand_{building.name}.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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