fix: small changes before the meeting

building_modelling/geojson_creator.py

@@ -4,16 +4,16 @@ from shapely import Point
 from pathlib import Path
 
 
-def process_geojson(x, y, diff, expansion=False):
+def process_geojson(x, y, diff, path, expansion=False):
   selection_box = Polygon([[x + diff, y - diff],
                            [x - diff, y - diff],
                            [x - diff, y + diff],
                            [x + diff, y + diff]])
-  geojson_file = Path('./data/collinear_clean 2.geojson').resolve()
+  geojson_file = Path(path / 'data/collinear_clean 2.geojson').resolve()
   if not expansion:
-    output_file = Path('./input_files/output_buildings.geojson').resolve()
+    output_file = Path(path / 'input_files/output_buildings.geojson').resolve()
   else:
-    output_file = Path('./input_files/output_buildings_expanded.geojson').resolve()
+    output_file = Path(path / 'input_files/output_buildings_expanded.geojson').resolve()
   buildings_in_region = []
 
   with open(geojson_file, 'r') as file:

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/electricity_demand_calculator.py

@@ -6,8 +6,8 @@ class HourlyElectricityDemand:
   def calculate(self):
     hourly_electricity_consumption = []
     energy_systems = self.building.energy_systems
-    appliance = self.building.appliances_electrical_demand[cte.HOUR]
-    lighting = self.building.lighting_electrical_demand[cte.HOUR]
+    appliance = self.building.appliances_electrical_demand[cte.HOUR] if self.building.appliances_electrical_demand else 0
+    lighting = self.building.lighting_electrical_demand[cte.HOUR] if self.building.lighting_electrical_demand else 0
     elec_heating = 0
     elec_cooling = 0
     elec_dhw = 0
@@ -59,10 +59,12 @@ class HourlyElectricityDemand:
               else:
                 cooling = self.building.cooling_consumption[cte.HOUR]
 
-    for i in range(len(self.building.heating_demand[cte.HOUR])):
+    for i in range(8760):
       hourly = 0
-      hourly += appliance[i]
-      hourly += lighting[i]
+      if isinstance(appliance, list):
+        hourly += appliance[i]
+      if isinstance(lighting, list):
+        hourly += lighting[i]
       if heating is not None:
         hourly += heating[i]
       if cooling is not None:

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/pv_system_assessment.py

@@ -0,0 +1,235 @@
+import math
+import csv
+import hub.helpers.constants as cte
+from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.electricity_demand_calculator import \
+  HourlyElectricityDemand
+from hub.catalog_factories.energy_systems_catalog_factory import EnergySystemsCatalogFactory
+from hub.helpers.monthly_values import MonthlyValues
+
+
+class PvSystemAssessment:
+  def __init__(self, building=None, pv_system=None, battery=None, tilt_angle=None, solar_angles=None,
+               system_type=None, pv_installation_type=None, simulation_model_type=None, module_model_name=None,
+               inverter_efficiency=None, system_catalogue_handler=None, roof_percentage_coverage=None,
+               facade_coverage_percentage=None, csv_output=False, output_path=None):
+    """
+    :param building:
+    :param tilt_angle:
+    :param solar_angles:
+    :param system_type:
+    :param simulation_model_type:
+    :param module_model_name:
+    :param inverter_efficiency:
+    :param system_catalogue_handler:
+    :param roof_percentage_coverage:
+    :param facade_coverage_percentage:
+    """
+    self.building = building
+    self.tilt_angle = tilt_angle
+    self.solar_angles = solar_angles
+    self.system_type = system_type
+    self.pv_installation_type = pv_installation_type
+    self.simulation_model_type = simulation_model_type
+    self.module_model_name = module_model_name
+    self.inverter_efficiency = inverter_efficiency
+    self.system_catalogue_handler = system_catalogue_handler
+    self.roof_percentage_coverage = roof_percentage_coverage
+    self.facade_coverage_percentage = facade_coverage_percentage
+    self.pv_hourly_generation = None
+    self.t_cell = None
+    self.results = {}
+    self.csv_output = csv_output
+    self.output_path = output_path
+    if pv_system is not None:
+      self.pv_system = pv_system
+    else:
+      for energy_system in self.building.energy_systems:
+        for generation_system in energy_system.generation_systems:
+          if generation_system.system_type == cte.PHOTOVOLTAIC:
+            self.pv_system = generation_system
+    if battery is not None:
+      self.battery = battery
+    else:
+      for energy_system in self.building.energy_systems:
+        for generation_system in energy_system.generation_systems:
+          if generation_system.system_type == cte.PHOTOVOLTAIC and generation_system.energy_storage_systems is not None:
+            for storage_system in generation_system.energy_storage_systems:
+              if storage_system.type_energy_stored == cte.ELECTRICAL:
+                self.battery = storage_system
+
+  @staticmethod
+  def explicit_model(standard_test_condition_maximum_power, standard_test_condition_radiation,
+                     cell_temperature_coefficient, standard_test_condition_cell_temperature, nominal_radiation,
+                     nominal_cell_temperature, nominal_ambient_temperature, inverter_efficiency, number_of_panels,
+                     irradiance, outdoor_temperature):
+    inverter_efficiency = inverter_efficiency
+    stc_power = float(standard_test_condition_maximum_power)
+    stc_irradiance = float(standard_test_condition_radiation)
+    cell_temperature_coefficient = float(cell_temperature_coefficient) / 100 if (
+        cell_temperature_coefficient is not None) else None
+    stc_t_cell = float(standard_test_condition_cell_temperature)
+    nominal_condition_irradiance = float(nominal_radiation)
+    nominal_condition_cell_temperature = float(nominal_cell_temperature)
+    nominal_t_out = float(nominal_ambient_temperature)
+    g_i = irradiance
+    t_out = outdoor_temperature
+    t_cell = []
+    pv_output = []
+    for i in range(len(g_i)):
+      t_cell.append((t_out[i] + (g_i[i] / nominal_condition_irradiance) *
+                     (nominal_condition_cell_temperature - nominal_t_out)))
+      pv_output.append((inverter_efficiency * number_of_panels * (stc_power * (g_i[i] / stc_irradiance) *
+                                                                  (1 - cell_temperature_coefficient *
+                                                                   (t_cell[i] - stc_t_cell)))))
+    return pv_output
+
+  def rooftop_sizing(self):
+    pv_system = self.pv_system
+    if self.module_model_name is not None:
+      self.system_assignation()
+    # System Sizing
+    module_width = float(pv_system.width)
+    module_height = float(pv_system.height)
+    roof_area = 0
+    for roof in self.building.roofs:
+      roof_area += roof.perimeter_area
+    pv_module_area = module_width * module_height
+    available_roof = (self.roof_percentage_coverage * roof_area)
+    # Inter-Row Spacing
+    winter_solstice = self.solar_angles[(self.solar_angles['AST'].dt.month == 12) &
+                                        (self.solar_angles['AST'].dt.day == 21) &
+                                        (self.solar_angles['AST'].dt.hour == 12)]
+    solar_altitude = winter_solstice['solar altitude'].values[0]
+    solar_azimuth = winter_solstice['solar azimuth'].values[0]
+    distance = ((module_height * math.sin(math.radians(self.tilt_angle)) * abs(math.cos(math.radians(solar_azimuth)))) / math.tan(math.radians(solar_altitude)))
+    distance = float(format(distance, '.2f'))
+    # Calculation of the number of panels
+    space_dimension = math.sqrt(available_roof)
+    space_dimension = float(format(space_dimension, '.2f'))
+    panels_per_row = math.ceil(space_dimension / module_width)
+    number_of_rows = math.ceil(space_dimension / distance)
+    total_number_of_panels = panels_per_row * number_of_rows
+    total_pv_area = total_number_of_panels * pv_module_area
+    self.building.roofs[0].installed_solar_collector_area = total_pv_area
+    return panels_per_row, number_of_rows
+
+  def system_assignation(self):
+    generation_units_catalogue = EnergySystemsCatalogFactory(self.system_catalogue_handler).catalog
+    catalog_pv_generation_equipments = [component for component in
+                                        generation_units_catalogue.entries('generation_equipments') if
+                                        component.system_type == 'photovoltaic']
+    selected_pv_module = None
+    for pv_module in catalog_pv_generation_equipments:
+      if self.module_model_name == pv_module.model_name:
+        selected_pv_module = pv_module
+    if selected_pv_module is None:
+      raise ValueError("No PV module with the provided model name exists in the catalogue")
+    for energy_system in self.building.energy_systems:
+      for idx, generation_system in enumerate(energy_system.generation_systems):
+        if generation_system.system_type == cte.PHOTOVOLTAIC:
+          new_system = selected_pv_module
+          # Preserve attributes that exist in the original but not in the new system
+          for attr in dir(generation_system):
+            # Skip private attributes and methods
+            if not attr.startswith('__') and not callable(getattr(generation_system, attr)):
+              if not hasattr(new_system, attr):
+                setattr(new_system, attr, getattr(generation_system, attr))
+          # Replace the old generation system with the new one
+          energy_system.generation_systems[idx] = new_system
+
+  def grid_tied_system(self):
+    building_hourly_electricity_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in
+                                          HourlyElectricityDemand(self.building).calculate()]
+    rooftop_pv_output = [0] * 8760
+    facade_pv_output = [0] * 8760
+    rooftop_number_of_panels = 0
+    if 'rooftop' in self.pv_installation_type.lower():
+      np, ns = self.rooftop_sizing()
+      if self.simulation_model_type == 'explicit':
+        rooftop_number_of_panels = np * ns
+        rooftop_pv_output = self.explicit_model(standard_test_condition_maximum_power=
+                                                float(self.pv_system.standard_test_condition_maximum_power),
+                                                standard_test_condition_radiation=
+                                                float(self.pv_system.standard_test_condition_radiation),
+                                                cell_temperature_coefficient=
+                                                float(self.pv_system.cell_temperature_coefficient) / 100,
+                                                standard_test_condition_cell_temperature=
+                                                float(self.pv_system.standard_test_condition_cell_temperature),
+                                                nominal_radiation=float(self.pv_system.nominal_radiation),
+                                                nominal_cell_temperature=float(
+                                                  self.pv_system.nominal_cell_temperature),
+                                                nominal_ambient_temperature=
+                                                float(self.pv_system.nominal_ambient_temperature),
+                                                inverter_efficiency=self.inverter_efficiency,
+                                                number_of_panels=rooftop_number_of_panels,
+                                                irradiance=self.building.roofs[0].global_irradiance_tilted[
+                                                  cte.HOUR],
+                                                outdoor_temperature=self.building.external_temperature[
+                                                  cte.HOUR])
+
+    total_hourly_pv_output = [rooftop_pv_output[i] + facade_pv_output[i] for i in range(8760)]
+    imported_electricity = [0] * 8760
+    exported_electricity = [0] * 8760
+    for i in range(8760):
+      transfer = total_hourly_pv_output[i] - building_hourly_electricity_demand[i]
+      if transfer > 0:
+        exported_electricity[i] = transfer
+      else:
+        imported_electricity[i] = abs(transfer)
+
+    results = {'building_name': self.building.name,
+               'total_floor_area_m2': self.building.thermal_zones_from_internal_zones[0].total_floor_area,
+               'roof_area_m2': self.building.roofs[0].perimeter_area, 'rooftop_panels': rooftop_number_of_panels,
+               'rooftop_panels_area_m2': self.building.roofs[0].installed_solar_collector_area,
+               'yearly_rooftop_ghi_kW/m2': self.building.roofs[0].global_irradiance[cte.YEAR][0] / 1000,
+               f'yearly_rooftop_tilted_radiation_{self.tilt_angle}_degree_kW/m2':
+                 self.building.roofs[0].global_irradiance_tilted[cte.YEAR][0] / 1000,
+               'yearly_rooftop_pv_production_kWh': sum(rooftop_pv_output) / 1000,
+               'specific_pv_production_kWh/kWp': sum(rooftop_pv_output) / (
+                   float(self.pv_system.standard_test_condition_maximum_power) * rooftop_number_of_panels),
+               'hourly_rooftop_poa_irradiance_W/m2': self.building.roofs[0].global_irradiance_tilted[cte.HOUR],
+               'hourly_rooftop_pv_output_W': rooftop_pv_output, 'T_out': self.building.external_temperature[cte.HOUR],
+               'building_electricity_demand_W': building_hourly_electricity_demand,
+               'total_hourly_pv_system_output_W': total_hourly_pv_output, 'import_from_grid_W': imported_electricity,
+               'export_to_grid_W': exported_electricity}
+    return results
+
+  def enrich(self):
+    if self.system_type.lower() == 'grid_tied':
+      self.results = self.grid_tied_system()
+    hourly_pv_output = self.results['total_hourly_pv_system_output_W']
+    self.building.onsite_electrical_production[cte.HOUR] = hourly_pv_output
+    self.building.onsite_electrical_production[cte.MONTH] = MonthlyValues.get_total_month(hourly_pv_output)
+    self.building.onsite_electrical_production[cte.YEAR] = [sum(hourly_pv_output)]
+    if self.csv_output:
+      self.save_to_csv(self.results, self.output_path, f'{self.building.name}_pv_system_analysis.csv')
+
+  @staticmethod
+  def save_to_csv(data, output_path, filename='rooftop_system_results.csv'):
+    # Separate keys based on whether their values are single values or lists
+    single_value_keys = [key for key, value in data.items() if not isinstance(value, list)]
+    list_value_keys = [key for key, value in data.items() if isinstance(value, list)]
+
+    # Check if all lists have the same length
+    list_lengths = [len(data[key]) for key in list_value_keys]
+    if not all(length == list_lengths[0] for length in list_lengths):
+      raise ValueError("All lists in the dictionary must have the same length")
+
+    # Get the length of list values (assuming all lists are of the same length, e.g., 8760 for hourly data)
+    num_rows = list_lengths[0] if list_value_keys else 1
+
+    # Open the CSV file for writing
+    with open(output_path / filename, mode='w', newline='') as csv_file:
+      writer = csv.writer(csv_file)
+      # Write single-value data as a header section
+      for key in single_value_keys:
+        writer.writerow([key, data[key]])
+      # Write an empty row for separation
+      writer.writerow([])
+      # Write the header for the list values
+      writer.writerow(list_value_keys)
+      # Write each row for the lists
+      for i in range(num_rows):
+        row = [data[key][i] for key in list_value_keys]
+        writer.writerow(row)
+

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/radiation_tilted.py

@@ -5,19 +5,18 @@ from hub.helpers.monthly_values import MonthlyValues
 
 
 class RadiationTilted:
-  def __init__(self, building, solar_angles, tilt_angle, ghi, solar_constant=1366.1, maximum_clearness_index=1,
+  def __init__(self, building, solar_angles, tilt_angle, solar_constant=1366.1, maximum_clearness_index=1,
                min_cos_zenith=0.065, maximum_zenith_angle=87):
     self.building = building
-    self.ghi = ghi
     self.tilt_angle = tilt_angle
-    self.zeniths = solar_angles['zenith'].tolist()[:-1]
-    self.incidents = solar_angles['incident angle'].tolist()[:-1]
-    self.date_time = solar_angles['DateTime'].tolist()[:-1]
-    self.ast = solar_angles['AST'].tolist()[:-1]
-    self.solar_azimuth = solar_angles['solar azimuth'].tolist()[:-1]
-    self.solar_altitude = solar_angles['solar altitude'].tolist()[:-1]
+    self.zeniths = solar_angles['zenith'].tolist()
+    self.incidents = solar_angles['incident angle'].tolist()
+    self.date_time = solar_angles['DateTime'].tolist()
+    self.ast = solar_angles['AST'].tolist()
+    self.solar_azimuth = solar_angles['solar azimuth'].tolist()
+    self.solar_altitude = solar_angles['solar altitude'].tolist()
     data = {'DateTime': self.date_time, 'AST': self.ast, 'solar altitude': self.solar_altitude, 'zenith': self.zeniths,
-            'solar azimuth': self.solar_azimuth, 'incident angle': self.incidents, 'ghi': self.ghi}
+            'solar azimuth': self.solar_azimuth, 'incident angle': self.incidents}
     self.df = pd.DataFrame(data)
     self.df['DateTime'] = pd.to_datetime(self.df['DateTime'])
     self.df['AST'] = pd.to_datetime(self.df['AST'])
@@ -30,81 +29,84 @@ class RadiationTilted:
     self.i_oh = []
     self.k_t = []
     self.fraction_diffuse = []
-    self.diffuse_horizontal = []
-    self.beam_horizontal = []
+    self.diffuse_hor = []
     self.dni = []
-    self.beam_tilted = []
-    self.diffuse_tilted = []
-    self.total_radiation_tilted = []
-    self.calculate()
+    self.tilted_diffuse = []
+    self.tilted_beam = []
+    self.total_tilted = []
 
   def dni_extra(self):
     for i in range(len(self.df)):
-      self.i_on.append(self.solar_constant * (1 + 0.033 * math.cos(math.radians(360 * self.df.index.dayofyear[i] / 365))))
+      self.i_on.append(self.solar_constant * (1 + 0.033 *
+                                              math.cos(math.radians(360 * self.df.index.dayofyear[i] / 365))))
+      self.i_oh.append(self.i_on[i] * max(math.cos(math.radians(self.zeniths[i])), self.min_cos_zenith))
 
     self.df['extraterrestrial normal radiation (Wh/m2)'] = self.i_on
-
-  def clearness_index(self):
-    for i in range(len(self.df)):
-      self.i_oh.append(self.i_on[i] * max(math.cos(math.radians(self.zeniths[i])), self.min_cos_zenith))
-      self.k_t.append(self.ghi[i] / self.i_oh[i])
-      self.k_t[i] = max(0, self.k_t[i])
-      self.k_t[i] = min(self.maximum_clearness_index, self.k_t[i])
     self.df['extraterrestrial radiation on horizontal (Wh/m2)'] = self.i_oh
-    self.df['clearness index'] = self.k_t
 
-  def diffuse_fraction(self):
-    for i in range(len(self.df)):
-      if self.k_t[i] <= 0.22:
-        self.fraction_diffuse.append(1 - 0.09 * self.k_t[i])
-      elif self.k_t[i] <= 0.8:
-        self.fraction_diffuse.append(0.9511 - 0.1604 * self.k_t[i] + 4.388 * self.k_t[i] ** 2 -
-                                     16.638 * self.k_t[i] ** 3 + 12.336 * self.k_t[i] ** 4)
-      else:
-        self.fraction_diffuse.append(0.165)
-      if self.zeniths[i] > self.maximum_zenith_angle:
-        self.fraction_diffuse[i] = 1
+  def clearness_index(self, ghi, i_oh):
+    k_t = ghi / i_oh
+    k_t = max(0, k_t)
+    k_t = min(self.maximum_clearness_index, k_t)
+    return k_t
 
-    self.df['diffuse fraction'] = self.fraction_diffuse
+  def diffuse_fraction(self, k_t, zenith):
+    if k_t <= 0.22:
+      fraction_diffuse = 1 - 0.09 * k_t
+    elif k_t <= 0.8:
+      fraction_diffuse = (0.9511 - 0.1604 * k_t + 4.388 * k_t ** 2 - 16.638 * k_t ** 3 + 12.336 * k_t ** 4)
+    else:
+      fraction_diffuse = 0.165
+    if zenith > self.maximum_zenith_angle:
+      fraction_diffuse = 1
+    return fraction_diffuse
 
-  def radiation_components_horizontal(self):
-    for i in range(len(self.df)):
-      self.diffuse_horizontal.append(self.ghi[i] * self.fraction_diffuse[i])
-      self.beam_horizontal.append(self.ghi[i] - self.diffuse_horizontal[i])
-      self.dni.append((self.ghi[i] - self.diffuse_horizontal[i]) / math.cos(math.radians(self.zeniths[i])))
-      if self.zeniths[i] > self.maximum_zenith_angle or self.dni[i] < 0:
-        self.dni[i] = 0
+  def radiation_components_horizontal(self, ghi, fraction_diffuse, zenith):
+    diffuse_horizontal = ghi * fraction_diffuse
+    dni = (ghi - diffuse_horizontal) / math.cos(math.radians(zenith))
+    if zenith > self.maximum_zenith_angle or dni < 0:
+      dni = 0
+    return diffuse_horizontal, dni
 
-    self.df['diffuse horizontal (Wh/m2)'] = self.diffuse_horizontal
-    self.df['dni (Wh/m2)'] = self.dni
-    self.df['beam horizontal (Wh/m2)'] = self.beam_horizontal
-
-  def radiation_components_tilted(self):
-    for i in range(len(self.df)):
-      self.beam_tilted.append(self.dni[i] * math.cos(math.radians(self.incidents[i])))
-      self.beam_tilted[i] = max(self.beam_tilted[i], 0)
-      self.diffuse_tilted.append(self.diffuse_horizontal[i] * ((1 + math.cos(math.radians(self.tilt_angle))) / 2))
-      self.total_radiation_tilted.append(self.beam_tilted[i] + self.diffuse_tilted[i])
-
-    self.df['beam tilted (Wh/m2)'] = self.beam_tilted
-    self.df['diffuse tilted (Wh/m2)'] = self.diffuse_tilted
-    self.df['total radiation tilted (Wh/m2)'] = self.total_radiation_tilted
-
-  def calculate(self) -> pd.DataFrame:
-    self.dni_extra()
-    self.clearness_index()
-    self.diffuse_fraction()
-    self.radiation_components_horizontal()
-    self.radiation_components_tilted()
-    return self.df
+  def radiation_components_tilted(self, diffuse_horizontal, dni, incident_angle):
+    beam_tilted = dni * math.cos(math.radians(incident_angle))
+    beam_tilted = max(beam_tilted, 0)
+    self.tilted_beam.append(beam_tilted)
+    diffuse_tilted = diffuse_horizontal * ((1 + math.cos(math.radians(self.tilt_angle))) / 2)
+    self.tilted_diffuse.append(diffuse_tilted)
+    total_radiation_tilted = beam_tilted + diffuse_tilted
+    return total_radiation_tilted
 
   def enrich(self):
-    tilted_radiation = self.total_radiation_tilted
-    self.building.roofs[0].global_irradiance_tilted[cte.HOUR] = tilted_radiation
+    self.dni_extra()
+    ghi = self.building.roofs[0].global_irradiance[cte.HOUR]
+    hourly_tilted_radiation = []
+    for i in range(len(ghi)):
+      k_t = self.clearness_index(ghi=ghi[i], i_oh=self.i_oh[i])
+      self.k_t.append(k_t)
+      fraction_diffuse = self.diffuse_fraction(k_t, self.zeniths[i])
+      self.fraction_diffuse.append(fraction_diffuse)
+      diffuse_horizontal, dni = self.radiation_components_horizontal(ghi=ghi[i],
+                                                                     fraction_diffuse=fraction_diffuse,
+                                                                     zenith=self.zeniths[i])
+      self.diffuse_hor.append(diffuse_horizontal)
+      self.dni.append(dni)
+
+      hourly_tilted_radiation.append(int(self.radiation_components_tilted(diffuse_horizontal=diffuse_horizontal,
+                                                                          dni=dni,
+                                                                          incident_angle=self.incidents[i])))
+      self.total_tilted.append(hourly_tilted_radiation[i])
+    self.building.roofs[0].global_irradiance_tilted[cte.HOUR] = hourly_tilted_radiation
     self.building.roofs[0].global_irradiance_tilted[cte.MONTH] = (
       MonthlyValues.get_total_month(self.building.roofs[0].global_irradiance_tilted[cte.HOUR]))
     self.building.roofs[0].global_irradiance_tilted[cte.YEAR] = \
         [sum(self.building.roofs[0].global_irradiance_tilted[cte.MONTH])]
-
-
-
+    self.df['k_t'] = self.k_t
+    self.df['diffuse_frac'] = self.fraction_diffuse
+    self.df['diff_hor'] = self.diffuse_hor
+    self.df['dni'] = self.dni
+    self.df['diff_tilted'] = self.tilted_diffuse
+    self.df['diff_beam'] = self.tilted_beam
+    self.df['total_tilted'] = self.total_tilted
+    self.df['ghi'] = ghi
+    self.df.to_csv(f'{self.building.name}_old_radiation.csv')

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/solar_angles.py

@@ -1,7 +1,6 @@
 import math
 import pandas as pd
 from datetime import datetime
-from pathlib import Path
 
 
 class CitySolarAngles:
@@ -28,7 +27,7 @@ class CitySolarAngles:
     self.incidents = []
     self.beam_tilted = []
     self.factor = []
-    self.times = pd.date_range(start='2023-01-01', end='2024-01-01', freq='h', tz=self.timezone)
+    self.times = pd.date_range(start='2023-01-01', end='2023-12-31 23:00', freq='h', tz=self.timezone)
     self.df = pd.DataFrame(index=self.times)
     self.day_of_year = self.df.index.dayofyear
 
@@ -44,8 +43,9 @@ class CitySolarAngles:
     lst = lst_hour + lst_minute / 60 + lst_second / 3600
 
     # Calculate Apparent Solar Time (AST) in decimal hours
+
     ast_decimal = lst + eot / 60 + self.longitude_correction / 60
-    ast_hours = int(ast_decimal)
+    ast_hours = int(ast_decimal) % 24  # Adjust hours to fit within 0–23 range
     ast_minutes = round((ast_decimal - ast_hours) * 60)
 
     # Ensure ast_minutes is within valid range

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/solar_calculator.py

@@ -0,0 +1,221 @@
+import math
+import pandas as pd
+from datetime import datetime
+import hub.helpers.constants as cte
+from hub.helpers.monthly_values import MonthlyValues
+
+
+class SolarCalculator:
+  def __init__(self, city, tilt_angle, surface_azimuth_angle, standard_meridian=-75,
+               solar_constant=1366.1, maximum_clearness_index=1, min_cos_zenith=0.065, maximum_zenith_angle=87):
+    """
+    A class to calculate the solar angles and solar irradiance on a tilted surface in the City
+    :param city: An object from the City class -> City
+    :param tilt_angle: tilt angle of surface -> float
+    :param surface_azimuth_angle: The orientation of the surface. 0 is North -> float
+    :param standard_meridian: A standard meridian is the meridian whose mean solar time is the basis of the time of day
+    observed in a time zone -> float
+    :param solar_constant: The amount of energy received by a given area one astronomical unit away from the Sun. It is
+    constant and must not be changed
+    :param maximum_clearness_index: This is used to calculate the diffuse fraction of the solar irradiance -> float
+    :param min_cos_zenith: This is needed to avoid unrealistic values in tilted irradiance calculations -> float
+    :param maximum_zenith_angle: This is needed to avoid negative values in tilted irradiance calculations -> float
+    """
+    self.city = city
+    self.location_latitude = city.latitude
+    self.location_longitude = city.longitude
+    self.location_latitude_rad = math.radians(self.location_latitude)
+    self.surface_azimuth_angle = surface_azimuth_angle
+    self.surface_azimuth_rad = math.radians(surface_azimuth_angle)
+    self.tilt_angle = tilt_angle
+    self.tilt_angle_rad = math.radians(tilt_angle)
+    self.standard_meridian = standard_meridian
+    self.longitude_correction = (self.location_longitude - standard_meridian) * 4
+    self.solar_constant = solar_constant
+    self.maximum_clearness_index = maximum_clearness_index
+    self.min_cos_zenith = min_cos_zenith
+    self.maximum_zenith_angle = maximum_zenith_angle
+    timezone_offset = int(-standard_meridian / 15)
+    self.timezone = f'Etc/GMT{"+" if timezone_offset < 0 else "-"}{abs(timezone_offset)}'
+    self.eot = []
+    self.ast = []
+    self.hour_angles = []
+    self.declinations = []
+    self.solar_altitudes = []
+    self.solar_azimuths = []
+    self.zeniths = []
+    self.incidents = []
+    self.i_on = []
+    self.i_oh = []
+    self.times = pd.date_range(start='2023-01-01', end='2023-12-31 23:00', freq='h', tz=self.timezone)
+    self.solar_angles = pd.DataFrame(index=self.times)
+    self.day_of_year = self.solar_angles.index.dayofyear
+
+  def solar_time(self, datetime_val, day_of_year):
+    b = (day_of_year - 81) * 2 * math.pi / 364
+    eot = 9.87 * math.sin(2 * b) - 7.53 * math.cos(b) - 1.5 * math.sin(b)
+    self.eot.append(eot)
+
+    # Calculate Local Solar Time (LST)
+    lst_hour = datetime_val.hour
+    lst_minute = datetime_val.minute
+    lst_second = datetime_val.second
+    lst = lst_hour + lst_minute / 60 + lst_second / 3600
+
+    # Calculate Apparent Solar Time (AST) in decimal hours
+    ast_decimal = lst + eot / 60 + self.longitude_correction / 60
+    ast_hours = int(ast_decimal) % 24  # Adjust hours to fit within 0–23 range
+    ast_minutes = round((ast_decimal - ast_hours) * 60)
+
+    # Ensure ast_minutes is within valid range
+    if ast_minutes == 60:
+      ast_hours += 1
+      ast_minutes = 0
+    elif ast_minutes < 0:
+      ast_minutes = 0
+    ast_time = datetime(year=datetime_val.year, month=datetime_val.month, day=datetime_val.day,
+                        hour=ast_hours, minute=ast_minutes)
+    self.ast.append(ast_time)
+    return ast_time
+
+  def declination_angle(self, day_of_year):
+    declination = 23.45 * math.sin(math.radians(360 / 365 * (284 + day_of_year)))
+    declination_radian = math.radians(declination)
+    self.declinations.append(declination)
+    return declination_radian
+
+  def hour_angle(self, ast_time):
+    hour_angle = ((ast_time.hour * 60 + ast_time.minute) - 720) / 4
+    hour_angle_radian = math.radians(hour_angle)
+    self.hour_angles.append(hour_angle)
+    return hour_angle_radian
+
+  def solar_altitude(self, declination_radian, hour_angle_radian):
+    solar_altitude_radians = math.asin(math.cos(self.location_latitude_rad) * math.cos(declination_radian) *
+                                       math.cos(hour_angle_radian) + math.sin(self.location_latitude_rad) *
+                                       math.sin(declination_radian))
+    solar_altitude = math.degrees(solar_altitude_radians)
+    self.solar_altitudes.append(solar_altitude)
+    return solar_altitude_radians
+
+  def zenith(self, solar_altitude_radians):
+    solar_altitude = math.degrees(solar_altitude_radians)
+    zenith_degree = 90 - solar_altitude
+    zenith_radian = math.radians(zenith_degree)
+    self.zeniths.append(zenith_degree)
+    return zenith_radian
+
+  def solar_azimuth_analytical(self, hourangle, declination, zenith):
+    numer = (math.cos(zenith) * math.sin(self.location_latitude_rad) - math.sin(declination))
+    denom = (math.sin(zenith) * math.cos(self.location_latitude_rad))
+    if math.isclose(denom, 0.0, abs_tol=1e-8):
+      cos_azi = 1.0
+    else:
+      cos_azi = numer / denom
+
+    cos_azi = max(-1.0, min(1.0, cos_azi))
+
+    sign_ha = math.copysign(1, hourangle)
+    solar_azimuth_radians = sign_ha * math.acos(cos_azi) + math.pi
+    solar_azimuth_degrees = math.degrees(solar_azimuth_radians)
+    self.solar_azimuths.append(solar_azimuth_degrees)
+    return solar_azimuth_radians
+
+  def incident_angle(self, solar_altitude_radians, solar_azimuth_radians):
+    incident_radian = math.acos(math.cos(solar_altitude_radians) *
+                                math.cos(abs(solar_azimuth_radians - self.surface_azimuth_rad)) *
+                                math.sin(self.tilt_angle_rad) + math.sin(solar_altitude_radians) *
+                                math.cos(self.tilt_angle_rad))
+    incident_angle_degrees = math.degrees(incident_radian)
+    self.incidents.append(incident_angle_degrees)
+    return incident_radian
+
+  def dni_extra(self, day_of_year, zenith_radian):
+    i_on = self.solar_constant * (1 + 0.033 * math.cos(math.radians(360 * day_of_year / 365)))
+    i_oh = i_on * max(math.cos(zenith_radian), self.min_cos_zenith)
+    self.i_on.append(i_on)
+    self.i_oh.append(i_oh)
+    return i_on, i_oh
+
+  def clearness_index(self, ghi, i_oh):
+    k_t = ghi / i_oh
+    k_t = max(0, k_t)
+    k_t = min(self.maximum_clearness_index, k_t)
+    return k_t
+
+  def diffuse_fraction(self, k_t, zenith):
+    if k_t <= 0.22:
+      fraction_diffuse = 1 - 0.09 * k_t
+    elif k_t <= 0.8:
+      fraction_diffuse = (0.9511 - 0.1604 * k_t + 4.388 * k_t ** 2 - 16.638 * k_t ** 3 + 12.336 * k_t ** 4)
+    else:
+      fraction_diffuse = 0.165
+    if zenith > self.maximum_zenith_angle:
+      fraction_diffuse = 1
+    return fraction_diffuse
+
+  def radiation_components_horizontal(self, ghi, fraction_diffuse, zenith):
+    diffuse_horizontal = ghi * fraction_diffuse
+    dni = (ghi - diffuse_horizontal) / math.cos(math.radians(zenith))
+    if zenith > self.maximum_zenith_angle or dni < 0:
+      dni = 0
+    return diffuse_horizontal, dni
+
+  def radiation_components_tilted(self, diffuse_horizontal, dni, incident_angle):
+    beam_tilted = dni * math.cos(math.radians(incident_angle))
+    beam_tilted = max(beam_tilted, 0)
+    diffuse_tilted = diffuse_horizontal * ((1 + math.cos(math.radians(self.tilt_angle))) / 2)
+    total_radiation_tilted = beam_tilted + diffuse_tilted
+    return total_radiation_tilted
+
+  def solar_angles_calculator(self, csv_output=False):
+    for i in range(len(self.times)):
+      datetime_val = self.times[i]
+      day_of_year = self.day_of_year[i]
+      declination_radians = self.declination_angle(day_of_year)
+      ast_time = self.solar_time(datetime_val, day_of_year)
+      hour_angle_radians = self.hour_angle(ast_time)
+      solar_altitude_radians = self.solar_altitude(declination_radians, hour_angle_radians)
+      zenith_radians = self.zenith(solar_altitude_radians)
+      solar_azimuth_radians = self.solar_azimuth_analytical(hour_angle_radians, declination_radians, zenith_radians)
+      self.incident_angle(solar_altitude_radians, solar_azimuth_radians)
+      self.dni_extra(day_of_year=day_of_year, zenith_radian=zenith_radians)
+    self.solar_angles['DateTime'] = self.times
+    self.solar_angles['AST'] = self.ast
+    self.solar_angles['hour angle'] = self.hour_angles
+    self.solar_angles['eot'] = self.eot
+    self.solar_angles['declination angle'] = self.declinations
+    self.solar_angles['solar altitude'] = self.solar_altitudes
+    self.solar_angles['zenith'] = self.zeniths
+    self.solar_angles['solar azimuth'] = self.solar_azimuths
+    self.solar_angles['incident angle'] = self.incidents
+    self.solar_angles['extraterrestrial normal radiation (Wh/m2)'] = self.i_on
+    self.solar_angles['extraterrestrial radiation on horizontal (Wh/m2)'] = self.i_oh
+    if csv_output:
+      self.solar_angles.to_csv('solar_angles_new.csv')
+
+  def tilted_irradiance_calculator(self):
+    if self.solar_angles.empty:
+      self.solar_angles_calculator()
+    for building in self.city.buildings:
+      hourly_tilted_irradiance = []
+      roof_ghi = building.roofs[0].global_irradiance[cte.HOUR]
+      for i in range(len(roof_ghi)):
+        k_t = self.clearness_index(ghi=roof_ghi[i], i_oh=self.i_oh[i])
+        fraction_diffuse = self.diffuse_fraction(k_t, self.zeniths[i])
+        diffuse_horizontal, dni = self.radiation_components_horizontal(ghi=roof_ghi[i],
+                                                                       fraction_diffuse=fraction_diffuse,
+                                                                       zenith=self.zeniths[i])
+        hourly_tilted_irradiance.append(int(self.radiation_components_tilted(diffuse_horizontal=diffuse_horizontal,
+                                                                             dni=dni,
+                                                                             incident_angle=self.incidents[i])))
+
+      building.roofs[0].global_irradiance_tilted[cte.HOUR] = hourly_tilted_irradiance
+      building.roofs[0].global_irradiance_tilted[cte.MONTH] = (MonthlyValues.get_total_month(
+        building.roofs[0].global_irradiance_tilted[cte.HOUR]))
+      building.roofs[0].global_irradiance_tilted[cte.YEAR] = [sum(building.roofs[0].global_irradiance_tilted[cte.MONTH])]
+
+
+
+
+

energy_system_modelling_package/random_assignation.py

@@ -29,19 +29,21 @@ residential_systems_percentage = {'system 1 gas': 15,
                                   'system 8 electricity': 35}
 
 residential_new_systems_percentage = {
-    'Central 4 Pipes Air to Water Heat Pump and Gas Boiler with Independent Water Heating and PV': 100,
-    'Central 4 Pipes Air to Water Heat Pump and electrical Boiler with Independent Water Heating and PV': 0,
-    'Central 4 Pipes Ground to Water Heat Pump and Gas Boiler with Independent Water Heating and PV': 0,
-    'Central 4 Pipes Ground to Water Heat Pump and electrical Boiler with Independent Water Heating and PV': 0,
-    'Central 4 Pipes Water to Water Heat Pump and Gas Boiler with Independent Water Heating and PV': 0,
-    'Central 4 Pipes Water to Water Heat Pump and electrical Boiler with Independent Water Heating and PV': 0,
-    'Central 4 Pipes Air to Water Heat Pump and Gas Boiler with Independent Water Heating': 0,
-    'Central 4 Pipes Air to Water Heat Pump and electrical Boiler with Independent Water Heating': 0,
-    'Central 4 Pipes Ground to Water Heat Pump and Gas Boiler with Independent Water Heating': 0,
-    'Central 4 Pipes Ground to Water Heat Pump and electrical Boiler with Independent Water Heating': 0,
-    'Central 4 Pipes Water to Water Heat Pump and Gas Boiler with Independent Water Heating': 0,
-    'Central 4 Pipes Water to Water Heat Pump and electrical Boiler with Independent Water Heating': 0,
-    'Rooftop PV System': 0
+    'Central Hydronic Air and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and PV': 50,
+    'Central Hydronic Air and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW and PV': 0,
+    'Central Hydronic Ground and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and PV': 0,
+    'Central Hydronic Ground and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW '
+    'and PV': 0,
+    'Central Hydronic Water and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and PV': 0,
+    'Central Hydronic Water and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW '
+    'and PV': 0,
+    'Central Hydronic Air and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
+    'Central Hydronic Air and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
+    'Central Hydronic Ground and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
+    'Central Hydronic Ground and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
+    'Central Hydronic Water and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
+    'Central Hydronic Water and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
+    'Grid-Tied PV System': 50
 }
 
 non_residential_systems_percentage = {'system 1 gas': 0,

example_codes/pv_system_assessment.py

@@ -0,0 +1,88 @@
+from pathlib import Path
+import subprocess
+from building_modelling.ep_run_enrich import energy_plus_workflow
+from energy_system_modelling_package import random_assignation
+from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.pv_system_assessment import \
+  PvSystemAssessment
+from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.solar_calculator import \
+  SolarCalculator
+from hub.imports.energy_systems_factory import EnergySystemsFactory
+from hub.imports.geometry_factory import GeometryFactory
+from hub.helpers.dictionaries import Dictionaries
+from hub.imports.construction_factory import ConstructionFactory
+from hub.imports.usage_factory import UsageFactory
+from hub.imports.weather_factory import WeatherFactory
+from hub.imports.results_factory import ResultFactory
+from building_modelling.geojson_creator import process_geojson
+from hub.exports.exports_factory import ExportsFactory
+
+# Define paths for input and output directories, ensuring directories are created if they do not exist
+main_path = Path(__file__).parent.parent.resolve()
+input_files_path = (Path(__file__).parent.parent / 'input_files')
+input_files_path.mkdir(parents=True, exist_ok=True)
+output_path = (Path(__file__).parent.parent / 'out_files').resolve()
+output_path.mkdir(parents=True, exist_ok=True)
+# Define specific paths for outputs from EnergyPlus and SRA (Simplified Radiosity Algorith) and PV calculation processes
+energy_plus_output_path = output_path / 'energy_plus_outputs'
+energy_plus_output_path.mkdir(parents=True, exist_ok=True)
+sra_output_path = output_path / 'sra_outputs'
+sra_output_path.mkdir(parents=True, exist_ok=True)
+pv_assessment_path = output_path / 'pv_outputs'
+pv_assessment_path.mkdir(parents=True, exist_ok=True)
+# Generate a GeoJSON file for city buildings based on latitude, longitude, and building dimensions
+geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, path=main_path, diff=0.0001)
+geojson_file_path = input_files_path / 'output_buildings.geojson'
+# Initialize a city object from the geojson file, mapping building functions using a predefined dictionary
+city = GeometryFactory(file_type='geojson',
+                       path=geojson_file_path,
+                       height_field='height',
+                       year_of_construction_field='year_of_construction',
+                       function_field='function',
+                       function_to_hub=Dictionaries().montreal_function_to_hub_function).city
+# Enrich city data with construction, usage, and weather information specific to the location
+ConstructionFactory('nrcan', city).enrich()
+UsageFactory('nrcan', city).enrich()
+WeatherFactory('epw', city).enrich()
+# Execute the EnergyPlus workflow to simulate building energy performance and generate output
+energy_plus_workflow(city, energy_plus_output_path)
+# Export the city data in SRA-compatible format to facilitate solar radiation assessment
+ExportsFactory('sra', city, sra_output_path).export()
+# Run SRA simulation using an external command, passing the generated SRA XML file path as input
+sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
+subprocess.run(['sra', str(sra_path)])
+# Enrich city data with SRA simulation results for subsequent analysis
+ResultFactory('sra', city, sra_output_path).enrich()
+# Assign PV system archetype name to the buildings in city
+random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
+# Enrich city model with Montreal future systems parameters
+EnergySystemsFactory('montreal_future', city).enrich()
+# # Initialize solar calculation parameters (e.g., azimuth, altitude) and compute irradiance and solar angles
+tilt_angle = 37
+solar_parameters = SolarCalculator(city=city,
+                                   surface_azimuth_angle=180,
+                                   tilt_angle=tilt_angle)
+solar_angles = solar_parameters.solar_angles  # Obtain solar angles for further analysis
+solar_parameters.tilted_irradiance_calculator()
+# # PV modelling building by building
+#List of available PV modules ['RE400CAA Pure 2', 'RE410CAA Pure 2', 'RE420CAA Pure 2', 'RE430CAA Pure 2',
+# 'REC600AA Pro M', 'REC610AA Pro M', 'REC620AA Pro M', 'REC630AA Pro M', 'REC640AA Pro M']
+for building in city.buildings:
+  PvSystemAssessment(building=building,
+                     pv_system=None,
+                     battery=None,
+                     tilt_angle=tilt_angle,
+                     solar_angles=solar_angles,
+                     system_type='grid_tied',
+                     pv_installation_type='rooftop',
+                     simulation_model_type='explicit',
+                     module_model_name=None,
+                     inverter_efficiency=0.95,
+                     system_catalogue_handler='montreal_future',
+                     roof_percentage_coverage=0.75,
+                     facade_coverage_percentage=0,
+                     csv_output=False,
+                     output_path=pv_assessment_path).enrich()
+
+print('test')
+
+

hub/city_model_structure/building.py

@@ -840,53 +840,55 @@ class Building(CityObject):
     Get energy consumption of different sectors
     return: dict
     """
-    fuel_breakdown = {cte.ELECTRICITY: {cte.LIGHTING: self.lighting_electrical_demand[cte.YEAR][0],
-                                        cte.APPLIANCES: self.appliances_electrical_demand[cte.YEAR][0]}}
+    fuel_breakdown = {cte.ELECTRICITY: {cte.LIGHTING: self.lighting_electrical_demand[cte.YEAR][0] if self.lighting_electrical_demand else 0,
+                                        cte.APPLIANCES: self.appliances_electrical_demand[cte.YEAR][0] if self.appliances_electrical_demand else 0}}
     energy_systems = self.energy_systems
-    for energy_system in energy_systems:
-      demand_types = energy_system.demand_types
-      generation_systems = energy_system.generation_systems
-      for demand_type in demand_types:
-        for generation_system in generation_systems:
-          if generation_system.system_type != cte.PHOTOVOLTAIC:
-            if generation_system.fuel_type not in fuel_breakdown:
-              fuel_breakdown[generation_system.fuel_type] = {}
-            if demand_type in generation_system.energy_consumption:
-              fuel_breakdown[f'{generation_system.fuel_type}'][f'{demand_type}'] = (
-                generation_system.energy_consumption)[f'{demand_type}'][cte.YEAR][0]
-            storage_systems = generation_system.energy_storage_systems
-            if storage_systems:
-              for storage_system in storage_systems:
-                if storage_system.type_energy_stored == 'thermal' and storage_system.heating_coil_capacity is not None:
-                  fuel_breakdown[cte.ELECTRICITY][f'{demand_type}'] += storage_system.heating_coil_energy_consumption[f'{demand_type}'][cte.YEAR][0]
-    #TODO: When simulation models of all energy system archetypes are created, this part can be removed
-    heating_fuels = []
-    dhw_fuels = []
-    for energy_system in self.energy_systems:
-      if cte.HEATING in energy_system.demand_types:
-        for generation_system in energy_system.generation_systems:
-          heating_fuels.append(generation_system.fuel_type)
-      if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
-        for generation_system in energy_system.generation_systems:
-          dhw_fuels.append(generation_system.fuel_type)
-    for key in fuel_breakdown:
-      if key == cte.ELECTRICITY and cte.COOLING not in fuel_breakdown[key]:
-        for energy_system in energy_systems:
-          if cte.COOLING in energy_system.demand_types and cte.COOLING not in fuel_breakdown[key]:
-            for generation_system in energy_system.generation_systems:
-              fuel_breakdown[generation_system.fuel_type][cte.COOLING] = self.cooling_consumption[cte.YEAR][0]
-      for fuel in heating_fuels:
-        if cte.HEATING not in fuel_breakdown[fuel]:
+    if energy_systems is not None:
+      for energy_system in energy_systems:
+        demand_types = energy_system.demand_types
+        generation_systems = energy_system.generation_systems
+        for demand_type in demand_types:
+          for generation_system in generation_systems:
+            if generation_system.system_type != cte.PHOTOVOLTAIC:
+              if generation_system.fuel_type not in fuel_breakdown:
+                fuel_breakdown[generation_system.fuel_type] = {}
+              if demand_type in generation_system.energy_consumption:
+                fuel_breakdown[f'{generation_system.fuel_type}'][f'{demand_type}'] = (
+                  generation_system.energy_consumption)[f'{demand_type}'][cte.YEAR][0]
+              storage_systems = generation_system.energy_storage_systems
+              if storage_systems:
+                for storage_system in storage_systems:
+                  if storage_system.type_energy_stored == 'thermal' and storage_system.heating_coil_energy_consumption:
+                    fuel_breakdown[cte.ELECTRICITY][f'{demand_type}'] += (
+                      storage_system.heating_coil_energy_consumption)[f'{demand_type}'][cte.YEAR][0]
+      #TODO: When simulation models of all energy system archetypes are created, this part can be removed
+      heating_fuels = []
+      dhw_fuels = []
+      for energy_system in self.energy_systems:
+        if cte.HEATING in energy_system.demand_types:
+          for generation_system in energy_system.generation_systems:
+            heating_fuels.append(generation_system.fuel_type)
+        if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
+          for generation_system in energy_system.generation_systems:
+            dhw_fuels.append(generation_system.fuel_type)
+      for key in fuel_breakdown:
+        if key == cte.ELECTRICITY and cte.COOLING not in fuel_breakdown[key]:
           for energy_system in energy_systems:
-            if cte.HEATING in energy_system.demand_types:
-              for generation_system in energy_system.generation_systems:
-                fuel_breakdown[generation_system.fuel_type][cte.HEATING] = self.heating_consumption[cte.YEAR][0]
-      for fuel in dhw_fuels:
-        if cte.DOMESTIC_HOT_WATER not in fuel_breakdown[fuel]:
-          for energy_system in energy_systems:
-            if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
-              for generation_system in energy_system.generation_systems:
-                fuel_breakdown[generation_system.fuel_type][cte.DOMESTIC_HOT_WATER] = self.domestic_hot_water_consumption[cte.YEAR][0]
+            if cte.COOLING in energy_system.demand_types and cte.COOLING not in fuel_breakdown[key]:
+              if self.cooling_consumption:
+                fuel_breakdown[energy_system.generation_systems[0].fuel_type][cte.COOLING] = self.cooling_consumption[cte.YEAR][0]
+        for fuel in heating_fuels:
+          if cte.HEATING not in fuel_breakdown[fuel]:
+            for energy_system in energy_systems:
+              if cte.HEATING in energy_system.demand_types:
+                if self.heating_consumption:
+                  fuel_breakdown[energy_system.generation_systems[0].fuel_type][cte.HEATING] = self.heating_consumption[cte.YEAR][0]
+        for fuel in dhw_fuels:
+          if cte.DOMESTIC_HOT_WATER not in fuel_breakdown[fuel]:
+            for energy_system in energy_systems:
+              if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
+                if self.domestic_hot_water_consumption:
+                  fuel_breakdown[energy_system.generation_systems[0].fuel_type][cte.DOMESTIC_HOT_WATER] = self.domestic_hot_water_consumption[cte.YEAR][0]
     self._fuel_consumption_breakdown = fuel_breakdown
     return self._fuel_consumption_breakdown
 

input_files/output_buildings_expanded.geojson

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-        "name": "01044601",
-        "address": "rue Victor-Hugo (MTL) 1626",
-        "function": "1000",
-        "height": 8,
-        "year_of_construction": 1986
-      }
-    },
-    {
-      "type": "Feature",
-      "geometry": {
-        "type": "Polygon",
-        "coordinates": [
-          [
-            [
-              -73.56790756893894,
-              45.492291541967774
-            ],
-            [
-              -73.56790222258577,
-              45.49229712328457
-            ],
-            [
-              -73.56803643080562,
-              45.49236123475947
-            ],
-            [
-              -73.56807651582375,
-              45.49231957685336
-            ],
-            [
-              -73.56794223597048,
-              45.4922554321734
-            ],
-            [
-              -73.56790756893894,
-              45.492291541967774
-            ]
-          ]
-        ]
-      },
-      "id": 182442,
-      "properties": {
-        "name": "01044609",
-        "address": "rue Victor-Hugo (MTL) 1646",
-        "function": "1000",
-        "height": 11,
-        "year_of_construction": 1986
-      }
-    },
-    {
-      "type": "Feature",
-      "geometry": {
-        "type": "Polygon",
-        "coordinates": [
-          [
-            [
-              -73.56829706912258,
-              45.49188914205178
-            ],
-            [
-              -73.56825635009473,
-              45.49193088860213
-            ],
-            [
-              -73.56838787594006,
-              45.49199371809223
-            ],
-            [
-              -73.56842846901456,
-              45.49195154234486
-            ],
-            [
-              -73.56829706912258,
-              45.49188914205178
-            ]
-          ]
-        ]
-      },
-      "id": 182546,
-      "properties": {
-        "name": "01044592",
-        "address": "rue Victor-Hugo (MTL) 1606",
-        "function": "1000",
-        "height": 8,
-        "year_of_construction": 1986
-      }
-    }
-  ]
-}

tests/test_systems_catalog.py

@@ -41,9 +41,9 @@ class TestSystemsCatalog(TestCase):
     archetypes = catalog.names()
     self.assertEqual(13, len(archetypes['archetypes']))
     systems = catalog.names('systems')
-    self.assertEqual(17, len(systems['systems']))
+    self.assertEqual(24, len(systems['systems']))
     generation_equipments = catalog.names('generation_equipments')
-    self.assertEqual(27, len(generation_equipments['generation_equipments']))
+    self.assertEqual(46, len(generation_equipments['generation_equipments']))
     with self.assertRaises(ValueError):
       catalog.names('unknown')
 
@@ -54,4 +54,6 @@ class TestSystemsCatalog(TestCase):
 
     with self.assertRaises(IndexError):
       catalog.get_entry('unknown')
-    print(catalog.entries())
+    catalog_pv_generation_equipments = [component for component in
+                                        catalog.entries('generation_equipments') if
+                                        component.system_type == cte.PHOTOVOLTAIC]