fix: all codes are updated for the Friday meeting

.gitignore

@@ -11,4 +11,4 @@
 **/.idea/
 cerc_hub.egg-info
 /out_files
-/input_files/output_buildings.geojson
+/input_files/output_buildings1.geojson

hub/city_model_structure/building.py

@@ -92,6 +92,7 @@ class Building(CityObject):
         logging.error('Building %s [%s] has an unexpected surface type %s.', self.name, self.aliases, surface.type)
     self._domestic_hot_water_peak_load = None
     self._fuel_consumption_breakdown = {}
+    self._pv_generation = {}
 
   @property
   def shell(self) -> Polyhedron:
@@ -903,10 +904,13 @@ class Building(CityObject):
         if cte.COOLING in energy_system.demand_types:
           for generation_system in energy_system.generation_systems:
             fuel_breakdown[generation_system.fuel_type][cte.COOLING] = self.cooling_consumption[cte.YEAR][0] / 3600
-
-
-
-
-
     self._fuel_consumption_breakdown = fuel_breakdown
     return self._fuel_consumption_breakdown
+
+  @property
+  def pv_generation(self) -> dict:
+    return self._pv_generation
+
+  @pv_generation.setter
+  def pv_generation(self, value):
+    self._pv_generation = value

hub/city_model_structure/building_demand/surface.py

@@ -46,6 +46,8 @@ class Surface:
     self._vegetation = None
     self._percentage_shared = None
     self._solar_collectors_area_reduction_factor = None
+    self._global_irradiance_tilted = {}
+    self._installed_solar_collector_area = None
 
   @property
   def name(self):
@@ -384,3 +386,35 @@ class Surface:
     :param value: float
     """
     self._solar_collectors_area_reduction_factor = value
+
+  @property
+  def global_irradiance_tilted(self) -> dict:
+    """
+    Get global irradiance on a tilted surface in J/m2
+    :return: dict
+    """
+    return self._global_irradiance_tilted
+
+  @global_irradiance_tilted.setter
+  def global_irradiance_tilted(self, value):
+    """
+    Set global irradiance on a tilted surface in J/m2
+    :param value: dict
+    """
+    self._global_irradiance_tilted = value
+
+  @property
+  def installed_solar_collector_area(self):
+    """
+    Get installed solar collector area in m2
+    :return: dict
+    """
+    return self._installed_solar_collector_area
+
+  @installed_solar_collector_area.setter
+  def installed_solar_collector_area(self, value):
+    """
+    Set installed solar collector area in m2
+    :return: dict
+    """
+    self._installed_solar_collector_area = value

hub/data/costs/montreal_costs_completed.xml

@@ -25,8 +25,8 @@
             <D_services>
                 <D20_onsite_generation>
                     <D2010_photovoltaic_system>
-                        <investment_cost cost_unit="currency/m2">    0    </investment_cost>
-                        <reposition cost_unit="currency/m2">       0         </reposition>
+                        <investment_cost cost_unit="currency/m2">    300    </investment_cost>
+                        <reposition cost_unit="currency/m2">       300         </reposition>
                         <lifetime_equipment lifetime="years">       25         </lifetime_equipment>
                     </D2010_photovoltaic_system>
                 </D20_onsite_generation>

main.py

@@ -18,12 +18,14 @@ from scripts.costs.cost import Cost
 from scripts.costs.constants import SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT_AND_PV
 import hub.helpers.constants as cte
 from hub.exports.exports_factory import ExportsFactory
+from scripts.pv_sizing_and_simulation import PVSizingSimulation
+from scripts.solar_angles import CitySolarAngles
 # Specify the GeoJSON file path
-# geojson_file = process_geojson(x=-73.5953602192335, y=45.492414530022515, diff=0.001)
-file_path = (Path(__file__).parent / 'input_files' / 'output_buildings.geojson')
+# geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.0001)
+file_path = (Path(__file__).parent / 'input_files' / 'output_buildings1.geojson')
 # Specify the output path for the PDF file
 output_path = (Path(__file__).parent / 'out_files').resolve()
-# Create city object from GeoJSON file
+# # Create city object from GeoJSON file
 city = GeometryFactory('geojson',
                        path=file_path,
                        height_field='height',
@@ -32,10 +34,19 @@ city = GeometryFactory('geojson',
                        function_to_hub=Dictionaries().montreal_function_to_hub_function).city
 # Enrich city data
 ConstructionFactory('nrcan', city).enrich()
-
 UsageFactory('nrcan', city).enrich()
 WeatherFactory('epw', city).enrich()
+ExportsFactory('obj', city, output_path).export()
+ExportsFactory('sra', city, output_path).export()
+sra_path = (output_path / f'{city.name}_sra.xml').resolve()
+subprocess.run(['sra', str(sra_path)])
+ResultFactory('sra', city, output_path).enrich()
 energy_plus_workflow(city)
+solar_angles = CitySolarAngles(city.name,
+                               city.latitude,
+                               city.longitude,
+                               tilt_angle=45,
+                               surface_azimuth_angle=180).calculate
 random_assignation.call_random(city.buildings, random_assignation.residential_systems_percentage)
 EnergySystemsFactory('montreal_custom', city).enrich()
 SystemSizing(city.buildings).montreal_custom()
@@ -44,9 +55,13 @@ random_assignation.call_random(city.buildings, random_assignation.residential_ne
 EnergySystemsFactory('montreal_future', city).enrich()
 for building in city.buildings:
   EnergySystemsSimulationFactory('archetype1', building=building, output_path=output_path).enrich()
-  print(building.energy_consumption_breakdown[cte.ELECTRICITY][cte.COOLING] +
-        building.energy_consumption_breakdown[cte.ELECTRICITY][cte.HEATING] +
-        building.energy_consumption_breakdown[cte.ELECTRICITY][cte.DOMESTIC_HOT_WATER])
+  pv_sizing_simulation = PVSizingSimulation(building,
+                                            solar_angles,
+                                            tilt_angle=45,
+                                            module_height=1,
+                                            module_width=2,
+                                            ghi=building.roofs[0].global_irradiance[cte.HOUR])
+  pv_sizing_simulation.pv_output()
 new_system = new_system_results(city.buildings)
 # EnergySystemAnalysisReport(city, output_path).create_report(current_system, new_system)
 for building in city.buildings:

pv_assessment.py

@@ -0,0 +1,69 @@
+import pandas as pd
+from scripts.geojson_creator import process_geojson
+from pathlib import Path
+import subprocess
+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 scripts.solar_angles import CitySolarAngles
+from scripts.ep_run_enrich import energy_plus_workflow
+import hub.helpers.constants as cte
+from hub.exports.exports_factory import ExportsFactory
+from scripts.pv_sizing_and_simulation import PVSizingSimulation
+# Specify the GeoJSON file path
+# geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.0001)
+file_path = (Path(__file__).parent / 'input_files' / 'output_buildings1.geojson')
+# Specify the output path for the PDF file
+output_path = (Path(__file__).parent / 'out_files').resolve()
+# Create city object from GeoJSON file
+city = GeometryFactory('geojson',
+                       path=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
+ConstructionFactory('nrcan', city).enrich()
+
+UsageFactory('nrcan', city).enrich()
+WeatherFactory('epw', city).enrich()
+ExportsFactory('obj', city, output_path).export()
+ExportsFactory('sra', city, output_path).export()
+sra_path = (output_path / f'{city.name}_sra.xml').resolve()
+subprocess.run(['sra', str(sra_path)])
+ResultFactory('sra', city, output_path).enrich()
+energy_plus_workflow(city)
+solar_angles = CitySolarAngles(city.name,
+                               city.latitude,
+                               city.longitude,
+                               tilt_angle=45,
+                               surface_azimuth_angle=180).calculate
+df = pd.DataFrame()
+df.index = ['yearly lighting (kWh)', 'yearly appliance (kWh)', 'yearly heating (kWh)', 'yearly cooling (kWh)',
+            'yearly dhw (kWh)', 'roof area (m2)', 'used area for pv (m2)', 'number of panels', 'pv production (kWh)']
+for building in city.buildings:
+  ghi = [x for x in building.roofs[0].global_irradiance[cte.HOUR]]
+  pv_sizing_simulation = PVSizingSimulation(building,
+                                            solar_angles,
+                                            tilt_angle=45,
+                                            module_height=1,
+                                            module_width=2,
+                                            ghi=ghi)
+  pv_sizing_simulation.pv_output()
+  yearly_lighting = building.lighting_electrical_demand[cte.YEAR][0] / 1000
+  yearly_appliance = building.appliances_electrical_demand[cte.YEAR][0] / 1000
+  yearly_heating = building.heating_demand[cte.YEAR][0] / (3.6e6 * 3)
+  yearly_cooling = building.cooling_demand[cte.YEAR][0] / (3.6e6 * 4.5)
+  yearly_dhw = building.domestic_hot_water_heat_demand[cte.YEAR][0] / 1000
+  roof_area = building.roofs[0].perimeter_area
+  used_roof = pv_sizing_simulation.available_space()
+  number_of_pv_panels = pv_sizing_simulation.total_number_of_panels
+  yearly_pv = building.onsite_electrical_production[cte.YEAR][0] / 1000
+  df[f'{building.name}'] = [yearly_lighting, yearly_appliance, yearly_heating, yearly_cooling, yearly_dhw, roof_area,
+                            used_roof, number_of_pv_panels, yearly_pv]
+
+df.to_csv(output_path / 'pv.csv')
+

scripts/costs/capital_costs.py

@@ -68,7 +68,10 @@ class CapitalCosts(CostBase):
     self._own_capital = 1 - self._configuration.percentage_credit
     self._surface_pv = 0
     for roof in self._building.roofs:
-      self._surface_pv += roof.solid_polygon.area * roof.solar_collectors_area_reduction_factor
+      if roof.installed_solar_collector_area is not None:
+        self._surface_pv += roof.installed_solar_collector_area
+      else:
+        self._surface_pv += roof.solid_polygon.area * roof.solar_collectors_area_reduction_factor
 
   def calculate(self) -> tuple[pd.DataFrame, pd.DataFrame]:
     if self._configuration.retrofit_scenario in (SKIN_RETROFIT, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV):
@@ -157,6 +160,7 @@ class CapitalCosts(CostBase):
     capital_cost_distribution_equipment = 0
     capital_cost_lighting = 0
     capital_cost_pv = self._surface_pv * chapter.item('D2010_photovoltaic_system').initial_investment[0]
+
     # capital_cost_lighting = self._total_floor_area * \
     #                         chapter.item('D5020_lighting_and_branch_wiring').initial_investment[0]
     for (i, component) in enumerate(system_components):

scripts/costs/total_maintenance_costs.py

@@ -39,8 +39,10 @@ class TotalMaintenanceCosts(CostBase):
     archetype = self._archetype
     # todo: change area pv when the variable exists
     roof_area = 0
+    surface_pv = 0
     for roof in building.roofs:
       roof_area += roof.solid_polygon.area
+      # surface_pv += roof.installed_solar_collector_area
     surface_pv = roof_area * 0.5
 
     peak_heating = building.heating_peak_load[cte.YEAR][0] / 3.6e6

scripts/costs/total_operational_incomes.py

@@ -37,7 +37,7 @@ class TotalOperationalIncomes(CostBase):
     for year in range(1, self._configuration.number_of_years + 1):
       price_increase_electricity = math.pow(1 + self._configuration.electricity_price_index, year)
       # todo: check the adequate assignation of price. Pilar
-      price_export = archetype.income.electricity_export * cte.WATTS_HOUR_TO_JULES * 1000  # to account for unit change
+      price_export = archetype.income.electricity_export  # to account for unit change
       self._yearly_operational_incomes.loc[year, 'Incomes electricity'] = (
           onsite_electricity_production * price_export * price_increase_electricity
       )

scripts/ep_run_enrich.py

@@ -15,9 +15,6 @@ def energy_plus_workflow(city):
     out_path = (Path(__file__).parent.parent / 'out_files')
     files = glob.glob(f'{out_path}/*')
 
-    # for file in files:
-    #   if file != '.gitignore':
-    #     os.remove(file)
     area = 0
     volume = 0
     for building in city.buildings:

scripts/geojson_creator.py

@@ -10,7 +10,7 @@ def process_geojson(x, y, diff):
                            [x - diff, y + diff],
                            [x + diff, y + diff]])
   geojson_file = Path('./data/collinear_clean 2.geojson').resolve()
-  output_file = Path('./input_files/output_buildings.geojson').resolve()
+  output_file = Path('./input_files/output_buildings1.geojson').resolve()
   buildings_in_region = []
 
   with open(geojson_file, 'r') as file:

scripts/pv_sizing_and_simulation.py

@@ -0,0 +1,58 @@
+import math
+
+from scripts.radiation_tilted import RadiationTilted
+import hub.helpers.constants as cte
+from hub.helpers.monthly_values import MonthlyValues
+
+
+class PVSizingSimulation(RadiationTilted):
+  def __init__(self, building, solar_angles, tilt_angle, module_height, module_width, ghi):
+    super().__init__(building, solar_angles, tilt_angle, ghi)
+    self.module_height = module_height
+    self.module_width = module_width
+    self.total_number_of_panels = 0
+    self.enrich()
+
+  def available_space(self):
+    roof_area = self.building.roofs[0].perimeter_area
+    maintenance_factor = 0.1
+    orientation_factor = 0.2
+    if self.building.function == cte.RESIDENTIAL:
+      mechanical_equipment_factor = 0.2
+    else:
+      mechanical_equipment_factor = 0.3
+    available_roof = (maintenance_factor + orientation_factor + mechanical_equipment_factor) * roof_area
+    return available_roof
+
+  def inter_row_spacing(self):
+    winter_solstice = self.df[(self.df['AST'].dt.month == 12) &
+                              (self.df['AST'].dt.day == 21) &
+                              (self.df['AST'].dt.hour == 12)]
+    solar_altitude = winter_solstice['solar altitude'].values[0]
+    solar_azimuth = winter_solstice['solar azimuth'].values[0]
+    distance = ((self.module_height * abs(math.cos(math.radians(solar_azimuth)))) /
+                math.tan(math.radians(solar_altitude)))
+    distance = float(format(distance, '.1f'))
+    return distance
+
+  def number_of_panels(self, available_roof, inter_row_distance):
+    space_dimension = math.sqrt(available_roof)
+    space_dimension = float(format(space_dimension, '.2f'))
+    panels_per_row = math.ceil(space_dimension / self.module_width)
+    number_of_rows = math.ceil(space_dimension / inter_row_distance)
+    self.total_number_of_panels = panels_per_row * number_of_rows
+    return panels_per_row, number_of_rows
+
+  def pv_output(self):
+    radiation = self.total_radiation_tilted
+    pv_module_area = self.module_width * self.module_height
+    available_roof = self.available_space()
+    inter_row_spacing = self.inter_row_spacing()
+    self.number_of_panels(available_roof, inter_row_spacing)
+    self.building.roofs[0].installed_solar_collector_area = pv_module_area * self.total_number_of_panels
+    system_efficiency = 0.2
+    pv_hourly_production = [x * system_efficiency * self.total_number_of_panels * pv_module_area for x in radiation]
+    self.building.onsite_electrical_production[cte.HOUR] = pv_hourly_production
+    self.building.onsite_electrical_production[cte.MONTH] = (
+      MonthlyValues.get_total_month(self.building.onsite_electrical_production[cte.HOUR]))
+    self.building.onsite_electrical_production[cte.YEAR] = [sum(self.building.onsite_electrical_production[cte.MONTH])]

scripts/radiation_tilted.py

@@ -0,0 +1,112 @@
+import pandas as pd
+import math
+import hub.helpers.constants as cte
+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,
+               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]
+    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}
+    self.df = pd.DataFrame(data)
+    self.df['DateTime'] = pd.to_datetime(self.df['DateTime'])
+    self.df['AST'] = pd.to_datetime(self.df['AST'])
+    self.df.set_index('DateTime', inplace=True)
+    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
+    self.i_on = []
+    self.i_oh = []
+    self.k_t = []
+    self.fraction_diffuse = []
+    self.diffuse_horizontal = []
+    self.beam_horizontal = []
+    self.dni = []
+    self.beam_tilted = []
+    self.diffuse_tilted = []
+    self.total_radiation_tilted = []
+    self.calculate()
+
+  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.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
+
+    self.df['diffuse fraction'] = self.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
+
+    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 enrich(self):
+    tilted_radiation = self.total_radiation_tilted
+    self.building.roofs[0].global_irradiance_tilted[cte.HOUR] = [x * cte.WATTS_HOUR_TO_JULES for x in
+                                                                 tilted_radiation]
+    self.building.roofs[0].global_irradiance_tilted[cte.HOUR] = [x * cte.WATTS_HOUR_TO_JULES for x in
+                                                                 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])]
+
+

scripts/solar_angles.py

@@ -0,0 +1,138 @@
+import math
+import pandas as pd
+from datetime import datetime
+from pathlib import Path
+
+
+class CitySolarAngles:
+  def __init__(self, file_name, location_latitude, location_longitude, tilt_angle, surface_azimuth_angle,
+               standard_meridian=-75):
+    self.file_name = file_name
+    self.location_latitude = location_latitude
+    self.location_longitude = location_longitude
+    self.location_latitude_rad = math.radians(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 = (location_longitude - standard_meridian) * 4
+    self.timezone = 'Etc/GMT+5'
+
+    self.eot = []
+    self.ast = []
+    self.hour_angles = []
+    self.declinations = []
+    self.solar_altitudes = []
+    self.solar_azimuths = []
+    self.zeniths = []
+    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.df = pd.DataFrame(index=self.times)
+    self.day_of_year = self.df.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)
+    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
+
+  @property
+  def calculate(self) -> pd.DataFrame:
+    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)
+      incident_angle_radian = self.incident_angle(solar_altitude_radians, solar_azimuth_radians)
+
+    self.df['DateTime'] = self.times
+    self.df['AST'] = self.ast
+    self.df['hour angle'] = self.hour_angles
+    self.df['eot'] = self.eot
+    self.df['declination angle'] = self.declinations
+    self.df['solar altitude'] = self.solar_altitudes
+    self.df['zenith'] = self.zeniths
+    self.df['solar azimuth'] = self.solar_azimuths
+    self.df['incident angle'] = self.incidents
+
+    return self.df