monthly_energy_balance_workflow/results.py

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()
                    ]
        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