Final version summercourse with final graphs. Pie chart mistake correction

input_files/eilat.geojson

@@ -0,0 +1,249 @@
+{
+  "type": "FeatureCollection",
+  "features": [
+    {
+      "type": "Feature",
+      "id": 1,
+      "properties": {
+        "heightmax": 9,
+        "ANNEE_CONS": 1978,
+        "CODE_UTILI": "residential"
+	  },
+      "geometry": {
+        "coordinates": [
+          [
+            [
+              34.95217088371581,
+              29.56694805860026
+            ],
+            [
+              34.95262396587913,
+              29.566952667742285
+            ],
+            [
+              34.95261999147337,
+              29.567024109421467
+            ],
+            [
+              34.952169558914704,
+              29.567019500282157
+            ],
+            [
+              34.95217088371581,
+              29.56694805860026
+            ]
+          ]
+        ],
+        "type": "Polygon"
+      }
+    },
+	{
+      "type": "Feature",
+      "id": 3,
+      "properties": {
+		"heightmax": 16,
+        "ANNEE_CONS": 2012,
+        "CODE_UTILI": "dormitory"
+	  },
+      "geometry": {
+        "coordinates": [
+          [
+            [
+              34.95176644317411,
+              29.56827388702702
+            ],
+            [
+              34.95176550020565,
+              29.568180388329026
+            ],
+            [
+              34.95179850408434,
+              29.568180388329026
+            ],
+            [
+              34.95179850408434,
+              29.5681303582886
+            ],
+            [
+              34.95176644317411,
+              29.5681303582886
+            ],
+            [
+              34.95176644317411,
+              29.568038499789708
+            ],
+            [
+              34.951874884488376,
+              29.568038499789708
+            ],
+            [
+              34.951874884488376,
+              29.568058183760357
+            ],
+            [
+              34.95192391882168,
+              29.568058183760357
+            ],
+            [
+              34.951922032885705,
+              29.56804178045124
+            ],
+            [
+              34.95205216246262,
+              29.568042600617147
+            ],
+            [
+              34.952051219494166,
+              29.568129538124154
+            ],
+            [
+              34.95201821561636,
+              29.5681303582886
+            ],
+            [
+              34.95201821561636,
+              29.568176287507143
+            ],
+            [
+              34.95204839059062,
+              29.568176287507143
+            ],
+            [
+              34.95205027652662,
+              29.56827552735433
+            ],
+            [
+              34.95195503676348,
+              29.568274707190284
+            ],
+            [
+              34.95195597973188,
+              29.56825830391628
+            ],
+            [
+              34.951849424353696,
+              29.56825830391628
+            ],
+            [
+              34.951849424353696,
+              29.568274707190284
+            ],
+            [
+              34.95176644317411,
+              29.56827388702702
+            ]
+          ]
+        ],
+        "type": "Polygon"
+      }
+    },
+    {
+      "type": "Feature",
+      "id": 2,
+      "properties": {
+		"heightmax": 24,
+        "ANNEE_CONS": 2002,
+        "CODE_UTILI": "Hotel employ"
+	  },
+      "geometry": {
+        "coordinates": [
+          [
+            [
+              34.94972280674813,
+              29.566224752287738
+            ],
+            [
+              34.94974316291999,
+              29.56597561012454
+            ],
+            [
+              34.94989147217407,
+              29.565980668855033
+            ],
+            [
+              34.94987402402688,
+              29.566233605043536
+            ],
+            [
+              34.94972280674813,
+              29.566224752287738
+            ]
+          ]
+        ],
+        "type": "Polygon"
+      }
+    },
+    {
+      "type": "Feature",
+      "id": 4,
+      "properties": {
+		"heightmax": 24,
+        "ANNEE_CONS": 2002,
+        "CODE_UTILI": "Hotel employ"
+	  },
+      "geometry": {
+        "coordinates": [
+          [
+            [
+              34.9492572120038,
+              29.565899295950373
+            ],
+            [
+              34.949411568175805,
+              29.565906093785898
+            ],
+            [
+              34.94939398329561,
+              29.56615761339161
+            ],
+            [
+              34.94924158099852,
+              29.566149116117728
+            ],
+            [
+              34.9492572120038,
+              29.565899295950373
+            ]
+          ]
+        ],
+        "type": "Polygon"
+      }
+    },
+    {
+      "type": "Feature",
+      "id": 5,
+      "properties": {
+		"heightmax": 9,
+        "ANNEE_CONS": 1978,
+        "CODE_UTILI": "residential"
+	  },
+      "geometry": {
+        "coordinates": [
+          [
+            [
+              34.95213398400861,
+              29.567197394967593
+            ],
+            [
+              34.95258172690612,
+              29.56720586098278
+            ],
+            [
+              34.95258172690612,
+              29.56727217807996
+            ],
+            [
+              34.952132361751325,
+              29.56726935607685
+            ],
+            [
+              34.95213398400861,
+              29.567197394967593
+            ]
+          ]
+        ],
+        "type": "Polygon"
+      }
+    }
+  ]
+}

main.py

@@ -12,9 +12,10 @@ from sra_engine import SraEngine
 
 
 try:
-  file_path = (Path(__file__).parent / 'input_files' / 'output_buildings.geojson')
+  file_path = (Path(__file__).parent / 'input_files' / 'eilat.geojson')
-  construction_format = 'nrcan'
+  climate_reference_city = 'Montreal'
-  usage_format = 'nrcan'
+  construction_format = 'eilat'
+  usage_format = 'eilat'
   energy_systems_format = 'montreal_custom'
 
   out_path = (Path(__file__).parent / 'output_files')
@@ -23,30 +24,26 @@ try:
   print('[simulation start]')
   city = GeometryFactory('geojson',
                          path=file_path,
-                         height_field='height',
+                         height_field='heightmax',
-                         year_of_construction_field='year_of_construction',
+                         year_of_construction_field='ANNEE_CONS',
-                         function_field='function',
+                         function_field='CODE_UTILI',
-                         function_to_hub=Dictionaries().montreal_function_to_hub_function).city
+                         function_to_hub=Dictionaries().eilat_function_to_hub_function).city
 
   print(f'city created from {file_path}')
   ConstructionFactory(construction_format, city).enrich()
   print('enrich constructions... done')
   UsageFactory(usage_format, city).enrich()
   print('enrich usage... done')
-  i = 1
-  x = len(city.buildings)
   for building in city.buildings:
-    if i < x:
+    building.energy_systems_archetype_name = 'system 1 gas pv'
-      building.energy_systems_archetype_name = 'system 1 gas'
-    else:
-      building.energy_systems_archetype_name = 'system 6 gas'
-    i = i + 1
   EnergySystemsFactory(energy_systems_format, city).enrich()
   print('enrich systems... done')
 
   print('exporting:')
-  SraEngine(city, tmp_folder)
+  sra_file = (tmp_folder / f'{city.name}_sra.xml').resolve()
+  SraEngine(city, sra_file, tmp_folder)
   print('    sra processed...')
+
   MonthlyEnergyBalanceEngine(city, tmp_folder)
   print('    insel processed...')
 

peak_load_class.py

@@ -0,0 +1,44 @@
+from pathlib import Path
+import pandas as pd
+import hub.helpers.constants as cte
+
+def Peak_load (building):
+  array = [None] * 12
+  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'])
+
+    return electricity_peak_load_results

results.py

@@ -9,50 +9,68 @@ class Results:
     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]
+        heating_results = building.heating_demand[cte.MONTH].rename(columns={cte.INSEL_MEB: f'{building.name} heating Wh'})
       else:
-        heating_results = [None] * 12
+        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]
+        cooling_results = building.cooling_demand[cte.MONTH].rename(columns={cte.INSEL_MEB: f'{building.name} cooling Wh'})
       else:
-        cooling_results = [None] * 12
+        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]
+        lighting_results = building.lighting_electrical_demand[cte.MONTH]\
+          .rename(columns={cte.INSEL_MEB: f'{building.name} lighting electrical demand Wh'})
       else:
-        lighting_results = [None] * 12
+        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]
+        appliances_results = building.appliances_electrical_demand[cte.MONTH]\
+          .rename(columns={cte.INSEL_MEB: f'{building.name} appliances electrical demand Wh'})
       else:
-        appliances_results = [None] * 12
+        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]
+        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 = [None] * 12
+        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 = building.heating_consumption[cte.MONTH]
+        heating_consumption_results = pd.DataFrame(building.heating_consumption[cte.MONTH],
+                                                   columns=[f'{building.name} heating consumption Wh'])
       else:
-        heating_consumption_results = [None] * 12
+        heating_consumption_results = pd.DataFrame(array, columns=[f'{building.name} heating consumption Wh'])
       if cte.MONTH in building.cooling_consumption.keys():
-        cooling_consumption_results = building.cooling_consumption[cte.MONTH]
+        cooling_consumption_results = pd.DataFrame(building.cooling_consumption[cte.MONTH],
+                                                   columns=[f'{building.name} cooling consumption Wh'])
       else:
-        cooling_consumption_results = [None] * 12
+        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 = building.domestic_hot_water_consumption[cte.MONTH]
+        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 = [None] * 12
+        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 = building.heating_peak_load[cte.MONTH]
+        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 = [None] * 12
+        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 = building.cooling_peak_load[cte.MONTH]
+        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 = [None] * 12
+        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:
@@ -64,17 +82,17 @@ class Results:
       if cte.MONTH in building.heating_peak_load.keys() and cte.MONTH in building.cooling_peak_load.keys():
         peak_lighting = 0
         peak_appliances = 0
-        thermal_zone = building.thermal_zones_from_internal_zones[0]
+        for thermal_zone in building.internal_zones[0].thermal_zones:
-        lighting = thermal_zone.lighting
+          lighting = thermal_zone.lighting
-        for schedule in lighting.schedules:
+          for schedule in lighting.schedules:
-          for value in schedule.values:
+            for value in schedule.values:
-            if value * lighting.density * thermal_zone.total_floor_area > peak_lighting:
+              if value * lighting.density * thermal_zone.total_floor_area > peak_lighting:
-              peak_lighting = value * lighting.density * thermal_zone.total_floor_area
+                peak_lighting = value * lighting.density * thermal_zone.total_floor_area
-        appliances = thermal_zone.appliances
+          appliances = thermal_zone.appliances
-        for schedule in appliances.schedules:
+          for schedule in appliances.schedules:
-          for value in schedule.values:
+            for value in schedule.values:
-            if value * appliances.density * thermal_zone.total_floor_area > peak_appliances:
+              if value * appliances.density * thermal_zone.total_floor_area > peak_appliances:
-              peak_appliances = value * appliances.density * thermal_zone.total_floor_area
+                peak_appliances = value * appliances.density * thermal_zone.total_floor_area
 
         monthly_electricity_peak = [0.9 * peak_lighting + 0.7 * peak_appliances] * 12
         conditioning_peak = []
@@ -85,49 +103,37 @@ class Results:
             conditioning_peak.append(heating * value)
           monthly_electricity_peak[i] += 0.8 * conditioning_peak[i]
 
-        electricity_peak_load_results = monthly_electricity_peak
+        electricity_peak_load_results = pd.DataFrame(monthly_electricity_peak
+                                                     , columns=[f'{building.name} electricity peak load W'])
       else:
-        electricity_peak_load_results = [None] * 12
+        electricity_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} electricity 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 = monthly_onsite_electrical_production
-      else:
-        onsite_electrical_production = [None] * 12
 
       if cte.MONTH in building.distribution_systems_electrical_consumption.keys():
-        extra_electrical_consumption = building.distribution_systems_electrical_consumption[cte.MONTH]
+        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 = [None] * 12
+        extra_electrical_consumption = pd.DataFrame(array,
+                                                    columns=[
+                                                      f'{building.name} electrical consumption for distribution Wh'])
 
-      columns_names = [f'{building.name} heating demand J',
+      if print_results is None:
-                       f'{building.name} cooling demand J',
+        print_results = heating_results
-                       f'{building.name} lighting demand J',
+      else:
-                       f'{building.name} appliances demand J',
+        print_results = pd.concat([print_results, heating_results], axis='columns')
-                       f'{building.name} domestic hot water demand J',
+      print_results = pd.concat([print_results,
-                       f'{building.name} heating consumption J',
+                                 cooling_results,
-                       f'{building.name} cooling consumption J',
+                                 lighting_results,
-                       f'{building.name} domestic hot water consumption J',
+                                 appliances_results,
-                       f'{building.name} heating peak load W',
+                                 dhw_results,
-                       f'{building.name} cooling peak load W',
+                                 heating_consumption_results,
-                       f'{building.name} electricity peak load W',
+                                 cooling_consumption_results,
-                       f'{building.name} onsite electrical production J',
+                                 dhw_consumption_results,
-                       f'{building.name} extra electrical consumption J'
+                                 heating_peak_load_results,
-                       ]
+                                 cooling_peak_load_results,
-      print_results = pd.DataFrame([heating_results,
+                                 electricity_peak_load_results,
-                                   cooling_results,
+                                 onsite_electrical_production,
-                                   lighting_results,
+                                 extra_electrical_consumption], axis='columns')
-                                   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]).T
-      print_results.columns = columns_names
       file += '\n'
       file += f'name: {building.name}\n'
       file += f'year of construction: {building.year_of_construction}\n'
@@ -139,8 +145,137 @@ class Results:
         file += f'storeys: n/a\n'
       file += f'volume: {building.volume}\n'
 
-      full_path_results = Path(self._path / f'demand_{building.name}.csv').resolve()
+    full_path_results = Path(self._path / 'demand.csv').resolve()
-      print_results.to_csv(full_path_results, na_rep='null')
+    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
+
+

sra_engine.py

@@ -7,13 +7,14 @@ from hub.imports.results_factory import ResultFactory
 
 
 class SraEngine:
-  def __init__(self, city, output_file_path):
+  def __init__(self, city, file_path, output_file_path):
     """
     SRA class
-    :param city: City
+    :param file_path: _sra.xml file path
     :param output_file_path: path to output the sra calculation
     """
     self._city = city
+    self._file_path = file_path
     self._output_file_path = output_file_path
     if platform.system() == 'Linux':
       self._executable = 'sra'
@@ -28,8 +29,6 @@ class SraEngine:
     Calls the software
     """
     try:
-      subprocess.run([self._executable,
+      subprocess.run([self._executable, str(self._file_path)], stdout=subprocess.DEVNULL)
-                     (self._output_file_path / f'{self._city.name}_sra.xml')],
-                     stdout=subprocess.DEVNULL)
     except (SubprocessError, TimeoutExpired, CalledProcessError) as error:
       raise Exception(error)