from pathlib import Path import pandas as pd import hub.helpers.constants as cte class Results: def __init__(self, city, path): self._city = city self._path = path def print(self): print_results = None file = 'city name: ' + self._city.name + '\n' array = [None] * 12 for building in self._city.buildings: if cte.MONTH in building.heating_demand.keys(): heating_results = building.heating_demand[cte.MONTH].rename(columns={cte.INSEL_MEB: f'{building.name} heating Wh'}) else: heating_results = pd.DataFrame(array, columns=[f'{building.name} heating demand Wh']) if cte.MONTH in building.cooling_demand.keys(): cooling_results = building.cooling_demand[cte.MONTH].rename(columns={cte.INSEL_MEB: f'{building.name} cooling Wh'}) else: cooling_results = pd.DataFrame(array, columns=[f'{building.name} cooling demand Wh']) if cte.MONTH in building.lighting_electrical_demand.keys(): lighting_results = building.lighting_electrical_demand[cte.MONTH]\ .rename(columns={cte.INSEL_MEB: f'{building.name} lighting electrical demand Wh'}) else: lighting_results = pd.DataFrame(array, columns=[f'{building.name} lighting electrical demand Wh']) if cte.MONTH in building.appliances_electrical_demand.keys(): appliances_results = building.appliances_electrical_demand[cte.MONTH]\ .rename(columns={cte.INSEL_MEB: f'{building.name} appliances electrical demand Wh'}) else: appliances_results = pd.DataFrame(array, columns=[f'{building.name} appliances electrical demand Wh']) if cte.MONTH in building.domestic_hot_water_heat_demand.keys(): dhw_results = building.domestic_hot_water_heat_demand[cte.MONTH]\ .rename(columns={cte.INSEL_MEB: f'{building.name} domestic hot water demand Wh'}) else: dhw_results = pd.DataFrame(array, columns=[f'{building.name} domestic hot water demand Wh']) if cte.MONTH in building.heating_consumption.keys(): heating_consumption_results = pd.DataFrame(building.heating_consumption[cte.MONTH], columns=[f'{building.name} heating consumption Wh']) else: heating_consumption_results = pd.DataFrame(array, columns=[f'{building.name} heating consumption Wh']) if cte.MONTH in building.cooling_consumption.keys(): cooling_consumption_results = pd.DataFrame(building.cooling_consumption[cte.MONTH], columns=[f'{building.name} cooling consumption Wh']) else: cooling_consumption_results = pd.DataFrame(array, columns=[f'{building.name} cooling consumption Wh']) if cte.MONTH in building.domestic_hot_water_consumption.keys(): dhw_consumption_results = pd.DataFrame(building.domestic_hot_water_consumption[cte.MONTH], columns=[f'{building.name} domestic hot water consumption Wh']) else: dhw_consumption_results = pd.DataFrame(array, columns=[f'{building.name} domestic hot water consumption Wh']) if cte.MONTH in building.heating_peak_load.keys(): heating_peak_load_results = pd.DataFrame(building.heating_peak_load[cte.MONTH], columns=[f'{building.name} heating peak load W']) else: heating_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} heating peak load W']) if cte.MONTH in building.cooling_peak_load.keys(): cooling_peak_load_results = pd.DataFrame(building.cooling_peak_load[cte.MONTH], columns=[f'{building.name} cooling peak load W']) else: cooling_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} cooling peak load W']) if cte.MONTH in building.onsite_electrical_production.keys(): monthly_onsite_electrical_production = building.onsite_electrical_production[cte.MONTH] onsite_electrical_production = pd.DataFrame(monthly_onsite_electrical_production, columns=[f'{building.name} onsite electrical production Wh']) else: onsite_electrical_production = pd.DataFrame(array, columns=[f'{building.name} onsite electrical production Wh']) heating = 0 cooling = 0 for system in building.energy_systems: for demand_type in system.demand_types: if demand_type == cte.HEATING: heating = 1 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, lighting_results, appliances_results, dhw_results, heating_consumption_results, cooling_consumption_results, dhw_consumption_results, heating_peak_load_results, cooling_peak_load_results, electricity_peak_load_results, onsite_electrical_production, extra_electrical_consumption], axis='columns') file += '\n' file += f'name: {building.name}\n' file += f'year of construction: {building.year_of_construction}\n' file += f'function: {building.function}\n' file += f'floor area: {building.floor_area}\n' if building.average_storey_height is not None and building.eave_height is not None: file += f'storeys: {int(building.eave_height / building.average_storey_height)}\n' else: file += f'storeys: n/a\n' file += f'volume: {building.volume}\n' full_path_results = Path(self._path / 'demand.csv').resolve() print_results.to_csv(full_path_results, na_rep='null') full_path_metadata = Path(self._path / 'metadata.csv').resolve() with open(full_path_metadata, 'w') as metadata_file: metadata_file.write(file) def outputsforgraph(self): outputs_energy =pd.DataFrame() array = [None] * 12 for building in self._city.buildings: if cte.MONTH in building.heating_demand.keys(): heating_results = building.heating_demand[cte.MONTH].rename( columns={cte.INSEL_MEB: f'{building.name} heating Wh'}) else: heating_results = pd.DataFrame(array, columns=[f'{building.name} heating demand Wh']) if cte.MONTH in building.cooling_demand.keys(): cooling_results = building.cooling_demand[cte.MONTH].rename( columns={cte.INSEL_MEB: f'{building.name} cooling Wh'}) else: cooling_results = pd.DataFrame(array, columns=[f'{building.name} cooling demand Wh']) if cte.MONTH in building.lighting_electrical_demand.keys(): lighting_results = building.lighting_electrical_demand[cte.MONTH] \ .rename(columns={cte.INSEL_MEB: f'{building.name} lighting electrical demand Wh'}) else: lighting_results = pd.DataFrame(array, columns=[f'{building.name} lighting electrical demand Wh']) if cte.MONTH in building.appliances_electrical_demand.keys(): appliances_results = building.appliances_electrical_demand[cte.MONTH] \ .rename(columns={cte.INSEL_MEB: f'{building.name} appliances electrical demand Wh'}) else: appliances_results = pd.DataFrame(array, columns=[f'{building.name} appliances electrical demand Wh']) if cte.MONTH in building.domestic_hot_water_heat_demand.keys(): dhw_results = building.domestic_hot_water_heat_demand[cte.MONTH] \ .rename(columns={cte.INSEL_MEB: f'{building.name} domestic hot water demand Wh'}) else: dhw_results = pd.DataFrame(array, columns=[f'{building.name} domestic hot water demand Wh']) if cte.MONTH in building.heating_consumption.keys(): heating_consumption_results = pd.DataFrame(building.heating_consumption[cte.MONTH], columns=[f'{building.name} heating consumption Wh']) else: heating_consumption_results = pd.DataFrame(array, columns=[f'{building.name} heating consumption Wh']) if cte.MONTH in building.cooling_consumption.keys(): cooling_consumption_results = pd.DataFrame(building.cooling_consumption[cte.MONTH], columns=[f'{building.name} cooling consumption Wh']) else: cooling_consumption_results = pd.DataFrame(array, columns=[f'{building.name} cooling consumption Wh']) if cte.MONTH in building.domestic_hot_water_consumption.keys(): dhw_consumption_results = pd.DataFrame(building.domestic_hot_water_consumption[cte.MONTH], columns=[f'{building.name} domestic hot water consumption Wh']) else: dhw_consumption_results = pd.DataFrame(array, columns=[f'{building.name} domestic hot water consumption Wh']) if cte.MONTH in building.heating_peak_load.keys(): heating_peak_load_results = pd.DataFrame(building.heating_peak_load[cte.MONTH], columns=[f'{building.name} heating peak load W']) else: heating_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} heating peak load W']) if cte.MONTH in building.cooling_peak_load.keys(): cooling_peak_load_results = pd.DataFrame(building.cooling_peak_load[cte.MONTH], columns=[f'{building.name} cooling peak load W']) else: cooling_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} cooling peak load W']) if cte.MONTH in building.onsite_electrical_production.keys(): monthly_onsite_electrical_production = building.onsite_electrical_production[cte.MONTH] onsite_electrical_production = pd.DataFrame(monthly_onsite_electrical_production, columns=[f'{building.name} onsite electrical production Wh']) else: onsite_electrical_production = pd.DataFrame(array, columns=[f'{building.name} onsite electrical production Wh']) heating = 0 cooling = 0 for system in building.energy_systems: for demand_type in system.demand_types: if demand_type == cte.HEATING: heating = 1 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']) listgraph = [lighting_results.values.sum(), appliances_results.values.sum(), heating_consumption_results.values.sum(), cooling_consumption_results.values.sum(), dhw_consumption_results.values.sum(), extra_electrical_consumption.values.sum() ] print(listgraph) outputs_energy[f'building {building.name}'] = listgraph outputs_energy.index = ['Lighting consumption', 'Appliances consumption', 'Heating consumption', 'Cooling consumption', 'DHW consumption', 'Extra electrical consumption'] total_final_energy = (lighting_results.values.sum() + appliances_results.values.sum() + \ heating_consumption_results.values.sum() + cooling_consumption_results.values.sum() + \ dhw_consumption_results.values.sum() + extra_electrical_consumption.values.sum())/1000 return outputs_energy, total_final_energy