Merge remote-tracking branch 'origin/main' into main

main.py

@@ -13,9 +13,8 @@ from sra_engine import SraEngine
 
 
 try:
-  file_path = (Path(__file__).parent / 'data' / 'selected_building.geojson')
+  file_path = (Path(__file__).parent / 'input_files' / 'selected_building_warehouse.geojson')
   climate_reference_city = 'Montreal'
-  weather_file = 'CAN_PQ_Montreal.Intl.AP.716270_CWEC.epw'
   weather_format = 'epw'
   construction_format = 'nrcan'
   usage_format = 'nrcan'
@@ -23,7 +22,7 @@ try:
   attic_heated_case = 0
   basement_heated_case = 1
 
-  out_path = (Path(__file__).parent / 'outputs')
+  out_path = (Path(__file__).parent / 'output_files')
   tmp_folder = (Path(__file__).parent / 'tmp')
 
   print('[simulation start]')
@@ -36,7 +35,7 @@ try:
   city.climate_reference_city = climate_reference_city
   city.climate_file = (tmp_folder / f'{climate_reference_city}.cli').resolve()
   print(f'city created from {file_path}')
-  WeatherFactory(weather_format, city, file_name=weather_file).enrich()
+  WeatherFactory(weather_format, city).enrich()
   print('enrich weather... done')
   ConstructionFactory(construction_format, city).enrich()
   print('enrich constructions... done')
@@ -49,7 +48,7 @@ try:
 
   print('exporting:')
   sra_file = (tmp_folder / f'{city.name}_sra.xml').resolve()
-  SraEngine(city, sra_file, tmp_folder, weather_file)
+  SraEngine(city, sra_file, tmp_folder)
   print('    sra processed...')
 
   MonthlyEnergyBalanceEngine(city, tmp_folder)
@@ -58,9 +57,9 @@ try:
   for building in city.buildings:
     for energy_system in building.energy_systems:
       if cte.HEATING in energy_system.demand_types:
-        energy_system.generation_system.heat_power = building.heating_peak_load[cte.YEAR][cte.HEATING_PEAK_LOAD][0]
+        energy_system.generation_system.heat_power = building.heating_peak_load[cte.YEAR][0]
       if cte.COOLING in energy_system.demand_types:
-        energy_system.generation_system.cooling_power = building.cooling_peak_load[cte.YEAR][cte.COOLING_PEAK_LOAD][0]
+        energy_system.generation_system.cooling_power = building.cooling_peak_load[cte.YEAR][0]
 
   print('importing results:')
   results = Results(city, out_path)

results.py

@@ -13,12 +13,12 @@ class Results:
     file = 'city name: ' + self._city.name + '\n'
     array = [None] * 12
     for building in self._city.buildings:
-      if cte.MONTH in building.heating.keys():
-        heating_results = building.heating[cte.MONTH].rename(columns={cte.INSEL_MEB: f'{building.name} heating Wh'})
+      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.keys():
-        cooling_results = building.cooling[cte.MONTH].rename(columns={cte.INSEL_MEB: f'{building.name} cooling 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():
@@ -54,11 +54,13 @@ class Results:
         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 = 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 = pd.DataFrame(array, columns=[f'{building.name} cooling peak load W'])
 
@@ -92,12 +94,11 @@ class Results:
               if value * appliances.density * thermal_zone.total_floor_area > peak_appliances:
                 peak_appliances = value * appliances.density * thermal_zone.total_floor_area
 
-        #todo:check with Pilar. Peak cannot be multiplied by 12. I deleted it. Is el. peak not saved in building?
-        monthly_electricity_peak = [0.9 * peak_lighting + 0.7 * peak_appliances]
+        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][cte.HEATING_PEAK_LOAD]):
-          if cooling * building.cooling_peak_load[cte.MONTH][cte.COOLING_PEAK_LOAD][i] > heating * value:
-            conditioning_peak.append(cooling * building.cooling_peak_load[cte.MONTH][cte.COOLING_PEAK_LOAD][i])
+        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]

sra_engine.py

@@ -7,7 +7,7 @@ from hub.imports.results_factory import ResultFactory
 
 
 class SraEngine:
-  def __init__(self, city, file_path, output_file_path, weather_file):
+  def __init__(self, city, file_path, output_file_path):
     """
     SRA class
     :param file_path: _sra.xml file path
@@ -16,12 +16,11 @@ class SraEngine:
     self._city = city
     self._file_path = file_path
     self._output_file_path = output_file_path
-    self._weather_file = weather_file
     if platform.system() == 'Linux':
       self._executable = 'sra'
     elif platform.system() == 'Windows':
       self._executable = 'shortwave_integer'
-    ExportsFactory('sra', self._city, output_file_path, weather_file=self._weather_file, weather_format='epw').export()
+    ExportsFactory('sra', self._city, output_file_path).export()
     self._run()
     ResultFactory('sra', self._city, output_file_path).enrich()