44 lines
1.9 KiB
Python
44 lines
1.9 KiB
Python
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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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def Peak_load (building):
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array = [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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for thermal_zone in building.internal_zones[0].thermal_zones:
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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 = pd.DataFrame(monthly_electricity_peak
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, columns=[f'{building.name} electricity peak load W'])
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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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return electricity_peak_load_results
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