fix: added radiation

hub/imports/geometry/geojson.py

@@ -135,6 +135,8 @@ class Geojson:
         building_aliases = []
         if 'id' in feature:
           building_name = feature['id']
+        elif 'id' in feature['properties']:
+          building_name = feature['properties']['id']
         else:
           building_name = uuid.uuid4()
         if self._aliases_field is not None:

main.py

@@ -7,9 +7,11 @@ from hub.imports.usage_factory import UsageFactory
 from hub.imports.weather_factory import WeatherFactory
 import hub.helpers.constants as cte
 from hub.imports.energy_systems_factory import EnergySystemsFactory
+from hub.exports.exports_factory import ExportsFactory
+import pandas as pd
 # Specify the GeoJSON file path
 input_files_path = (Path(__file__).parent / 'input_files')
-geojson_file_path = input_files_path / 'test.geojson'
+geojson_file_path = input_files_path / 'Lachine_New_Developments.geojson'
 output_path = (Path(__file__).parent / 'out_files').resolve()
 output_path.mkdir(parents=True, exist_ok=True)
 # Create city object from GeoJSON file
@@ -23,6 +25,7 @@ city = GeometryFactory('geojson',
 ConstructionFactory('nrcan', city).enrich()
 UsageFactory('nrcan', city).enrich()
 WeatherFactory('epw', city).enrich()
+ExportsFactory('obj', city, output_path).export()
 energy_plus_workflow(city)
 for building in city.buildings:
   print(building.heating_demand[cte.YEAR][0] / 3.6e6)
@@ -42,16 +45,22 @@ for building in city.buildings:
     if cte.HEATING in energy_system.demand_types:
       for generation_unit in generation_units:
         generation_unit.heat_efficiency = 0.96
-# for building in city.buildings:
-#   building.function = cte.COMMERCIAL
-#
-# ConstructionFactory('nrcan', city).enrich()
-# UsageFactory('nrcan', city).enrich()
-# energy_plus_workflow(city)
-# for building in city.buildings:
-#   print(building.heating_demand[cte.YEAR][0] / 3.6e6)
-#   print(building.name)
-#   total_floor_area = 0
-#   for thermal_zone in building.thermal_zones_from_internal_zones:
-#     total_floor_area += thermal_zone.total_floor_area
-#   print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
+for building in city.buildings:
+  building.function = cte.COMMERCIAL
+
+ConstructionFactory('nrcan', city).enrich()
+UsageFactory('nrcan', city).enrich()
+energy_plus_workflow(city)
+for building in city.buildings:
+  print(building.heating_demand[cte.YEAR][0] / 3.6e6)
+  print(building.name)
+  total_floor_area = 0
+  for thermal_zone in building.thermal_zones_from_internal_zones:
+    total_floor_area += thermal_zone.total_floor_area
+  print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
+building_names = []
+for building in city.buildings:
+  building_names.append(building.name)
+
+df = pd.DataFrame(columns=building_names)
+df.to_csv('selected_buildings.csv')

scripts/radiation_tilted.py

@@ -0,0 +1,113 @@
+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 +1,146 @@
+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
+
+
+
+
+
+
+
+

selected_buildings.csv

@@ -0,0 +1 @@
+,1673,1646,1647,1648,1649,1650,1651,1652,1653,1654,1655,1656,1657,1658,1659,1660,1661,1662,1663,1664,1665,1666,1667,1668,1669,1676,1677,1678,1679,1680,1681,1687,1674,1688,1675,1689,1690,1691

solar_radiation.py

@@ -0,0 +1,59 @@
+from pathlib import Path
+import subprocess
+from scripts.ep_workflow import energy_plus_workflow
+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
+import hub.helpers.constants as cte
+from hub.exports.exports_factory import ExportsFactory
+from hub.imports.results_factory import ResultFactory
+import pandas as pd
+from scripts.solar_angles import CitySolarAngles
+from scripts.radiation_tilted import RadiationTilted
+# Specify the GeoJSON file path
+input_files_path = (Path(__file__).parent / 'input_files')
+geojson_file_path = input_files_path / 'Lachine_New_Developments.geojson'
+output_path = (Path(__file__).parent / 'out_files').resolve()
+output_path.mkdir(parents=True, exist_ok=True)
+sra_output_path = output_path / 'sra_outputs'
+sra_output_path.mkdir(parents=True, exist_ok=True)
+# Create city object from GeoJSON file
+city = GeometryFactory('geojson',
+                       path=geojson_file_path,
+                       height_field='maximum_roof_height',
+                       year_of_construction_field='year_built',
+                       function_field='building_type',
+                       function_to_hub=Dictionaries().montreal_function_to_hub_function).city
+# Enrich city data
+WeatherFactory('epw', city).enrich()
+ExportsFactory('sra', city, sra_output_path).export()
+sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
+subprocess.run(['sra', str(sra_path)])
+ResultFactory('sra', city, sra_output_path).enrich()
+solar_angles = CitySolarAngles(city.name,
+                               city.latitude,
+                               city.longitude,
+                               tilt_angle=45,
+                               surface_azimuth_angle=180).calculate
+for building in city.buildings:
+  ghi = [x / cte.WATTS_HOUR_TO_JULES for x in building.roofs[0].global_irradiance[cte.HOUR]]
+  RadiationTilted(building,
+                  solar_angles,
+                  tilt_angle=45,
+                  ghi=ghi).enrich()
+building_names = []
+for building in city.buildings:
+  building_names.append(building.name)
+
+df = pd.DataFrame(columns=building_names)
+df1 = pd.DataFrame(columns=building_names)
+print('test')
+for building in city.buildings:
+  # if building.name in selected_buildings_list:
+  df[f'{building.name}'] = building.roofs[0].global_irradiance[cte.HOUR]
+  df1[f'{building.name}'] = building.roofs[0].global_irradiance_tilted[cte.HOUR]
+
+df.to_csv('solar_radiation_horizontal_selected_buildings.csv')
+df1.to_csv('solar_radiation_tilted_selected_buildings.csv')