added more results to the list
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2
main.py
2
main.py
@ -43,7 +43,7 @@ try:
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UsageFactory(usage_format, city).enrich()
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print('enrich usage... done')
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for building in city.buildings:
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building.energy_systems_archetype_name = 'system 1 gas'
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building.energy_systems_archetype_name = 'system 1 gas pv'
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EnergySystemsFactory(energy_systems_format, city).enrich()
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print('enrich systems... done')
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59
results.py
59
results.py
@ -57,12 +57,64 @@ class Results:
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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 = 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 = building.cooling_peak_load[cte.MONTH]
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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, 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:
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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]['heating peak loads']):
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if cooling * building.cooling_peak_load[cte.MONTH]['cooling peak loads'][i] > heating * value:
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conditioning_peak.append(cooling * building.cooling_peak_load[cte.MONTH]['cooling peak loads'][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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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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columns=[
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f'{building.name} electrical consumption for distribution Wh'])
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else:
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extra_electrical_consumption = pd.DataFrame(array,
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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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print_results = heating_results
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else:
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@ -76,7 +128,10 @@ class 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], axis='columns')
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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], 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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