fix: added radiation
This commit is contained in:
parent
73a7a12a97
commit
36ebbdc165
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@ -135,6 +135,8 @@ class Geojson:
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building_aliases = []
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building_aliases = []
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if 'id' in feature:
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if 'id' in feature:
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building_name = feature['id']
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building_name = feature['id']
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elif 'id' in feature['properties']:
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building_name = feature['properties']['id']
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else:
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else:
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building_name = uuid.uuid4()
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building_name = uuid.uuid4()
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if self._aliases_field is not None:
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if self._aliases_field is not None:
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5128
input_files/Lachine_New_Developments_All_Buildings.geojson
Normal file
5128
input_files/Lachine_New_Developments_All_Buildings.geojson
Normal file
File diff suppressed because it is too large
Load Diff
37
main.py
37
main.py
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@ -7,9 +7,11 @@ from hub.imports.usage_factory import UsageFactory
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from hub.imports.weather_factory import WeatherFactory
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from hub.imports.weather_factory import WeatherFactory
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import hub.helpers.constants as cte
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import hub.helpers.constants as cte
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from hub.imports.energy_systems_factory import EnergySystemsFactory
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from hub.imports.energy_systems_factory import EnergySystemsFactory
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from hub.exports.exports_factory import ExportsFactory
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import pandas as pd
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# Specify the GeoJSON file path
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# Specify the GeoJSON file path
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input_files_path = (Path(__file__).parent / 'input_files')
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input_files_path = (Path(__file__).parent / 'input_files')
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geojson_file_path = input_files_path / 'test.geojson'
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geojson_file_path = input_files_path / 'Lachine_New_Developments.geojson'
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output_path = (Path(__file__).parent / 'out_files').resolve()
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output_path = (Path(__file__).parent / 'out_files').resolve()
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output_path.mkdir(parents=True, exist_ok=True)
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output_path.mkdir(parents=True, exist_ok=True)
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# Create city object from GeoJSON file
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# Create city object from GeoJSON file
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@ -23,6 +25,7 @@ city = GeometryFactory('geojson',
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ConstructionFactory('nrcan', city).enrich()
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ConstructionFactory('nrcan', city).enrich()
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UsageFactory('nrcan', city).enrich()
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UsageFactory('nrcan', city).enrich()
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WeatherFactory('epw', city).enrich()
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WeatherFactory('epw', city).enrich()
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ExportsFactory('obj', city, output_path).export()
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energy_plus_workflow(city)
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energy_plus_workflow(city)
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for building in city.buildings:
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for building in city.buildings:
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print(building.heating_demand[cte.YEAR][0] / 3.6e6)
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print(building.heating_demand[cte.YEAR][0] / 3.6e6)
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@ -42,16 +45,22 @@ for building in city.buildings:
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if cte.HEATING in energy_system.demand_types:
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if cte.HEATING in energy_system.demand_types:
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for generation_unit in generation_units:
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for generation_unit in generation_units:
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generation_unit.heat_efficiency = 0.96
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generation_unit.heat_efficiency = 0.96
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# for building in city.buildings:
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for building in city.buildings:
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# building.function = cte.COMMERCIAL
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building.function = cte.COMMERCIAL
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#
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# ConstructionFactory('nrcan', city).enrich()
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ConstructionFactory('nrcan', city).enrich()
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# UsageFactory('nrcan', city).enrich()
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UsageFactory('nrcan', city).enrich()
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# energy_plus_workflow(city)
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energy_plus_workflow(city)
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# for building in city.buildings:
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for building in city.buildings:
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# print(building.heating_demand[cte.YEAR][0] / 3.6e6)
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print(building.heating_demand[cte.YEAR][0] / 3.6e6)
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# print(building.name)
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print(building.name)
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# total_floor_area = 0
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total_floor_area = 0
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# for thermal_zone in building.thermal_zones_from_internal_zones:
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for thermal_zone in building.thermal_zones_from_internal_zones:
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# total_floor_area += thermal_zone.total_floor_area
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total_floor_area += thermal_zone.total_floor_area
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# print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
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print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
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building_names = []
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for building in city.buildings:
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building_names.append(building.name)
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df = pd.DataFrame(columns=building_names)
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df.to_csv('selected_buildings.csv')
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113
scripts/radiation_tilted.py
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113
scripts/radiation_tilted.py
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@ -0,0 +1,113 @@
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import pandas as pd
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import math
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import hub.helpers.constants as cte
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from hub.helpers.monthly_values import MonthlyValues
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class RadiationTilted:
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def __init__(self, building, solar_angles, tilt_angle, ghi, solar_constant=1366.1, maximum_clearness_index=1,
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min_cos_zenith=0.065, maximum_zenith_angle=87):
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self.building = building
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self.ghi = ghi
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self.tilt_angle = tilt_angle
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self.zeniths = solar_angles['zenith'].tolist()[:-1]
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self.incidents = solar_angles['incident angle'].tolist()[:-1]
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self.date_time = solar_angles['DateTime'].tolist()[:-1]
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self.ast = solar_angles['AST'].tolist()[:-1]
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self.solar_azimuth = solar_angles['solar azimuth'].tolist()[:-1]
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self.solar_altitude = solar_angles['solar altitude'].tolist()[:-1]
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data = {'DateTime': self.date_time, 'AST': self.ast, 'solar altitude': self.solar_altitude, 'zenith': self.zeniths,
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'solar azimuth': self.solar_azimuth, 'incident angle': self.incidents, 'ghi': self.ghi}
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self.df = pd.DataFrame(data)
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self.df['DateTime'] = pd.to_datetime(self.df['DateTime'])
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self.df['AST'] = pd.to_datetime(self.df['AST'])
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self.df.set_index('DateTime', inplace=True)
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self.solar_constant = solar_constant
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self.maximum_clearness_index = maximum_clearness_index
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self.min_cos_zenith = min_cos_zenith
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self.maximum_zenith_angle = maximum_zenith_angle
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self.i_on = []
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self.i_oh = []
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self.k_t = []
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self.fraction_diffuse = []
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self.diffuse_horizontal = []
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self.beam_horizontal = []
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self.dni = []
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self.beam_tilted = []
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self.diffuse_tilted = []
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self.total_radiation_tilted = []
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self.calculate()
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def dni_extra(self):
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for i in range(len(self.df)):
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self.i_on.append(self.solar_constant * (1 + 0.033 * math.cos(math.radians(360 * self.df.index.dayofyear[i] / 365))))
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self.df['extraterrestrial normal radiation (Wh/m2)'] = self.i_on
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def clearness_index(self):
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for i in range(len(self.df)):
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self.i_oh.append(self.i_on[i] * max(math.cos(math.radians(self.zeniths[i])), self.min_cos_zenith))
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self.k_t.append(self.ghi[i] / self.i_oh[i])
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self.k_t[i] = max(0, self.k_t[i])
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self.k_t[i] = min(self.maximum_clearness_index, self.k_t[i])
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self.df['extraterrestrial radiation on horizontal (Wh/m2)'] = self.i_oh
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self.df['clearness index'] = self.k_t
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def diffuse_fraction(self):
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for i in range(len(self.df)):
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if self.k_t[i] <= 0.22:
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self.fraction_diffuse.append(1 - 0.09 * self.k_t[i])
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elif self.k_t[i] <= 0.8:
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self.fraction_diffuse.append(0.9511 - 0.1604 * self.k_t[i] + 4.388 * self.k_t[i] ** 2 -
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16.638 * self.k_t[i] ** 3 + 12.336 * self.k_t[i] ** 4)
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else:
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self.fraction_diffuse.append(0.165)
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if self.zeniths[i] > self.maximum_zenith_angle:
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self.fraction_diffuse[i] = 1
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self.df['diffuse fraction'] = self.fraction_diffuse
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def radiation_components_horizontal(self):
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for i in range(len(self.df)):
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self.diffuse_horizontal.append(self.ghi[i] * self.fraction_diffuse[i])
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self.beam_horizontal.append(self.ghi[i] - self.diffuse_horizontal[i])
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self.dni.append((self.ghi[i] - self.diffuse_horizontal[i]) / math.cos(math.radians(self.zeniths[i])))
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if self.zeniths[i] > self.maximum_zenith_angle or self.dni[i] < 0:
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self.dni[i] = 0
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self.df['diffuse horizontal (Wh/m2)'] = self.diffuse_horizontal
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self.df['dni (Wh/m2)'] = self.dni
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self.df['beam horizontal (Wh/m2)'] = self.beam_horizontal
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def radiation_components_tilted(self):
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for i in range(len(self.df)):
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self.beam_tilted.append(self.dni[i] * math.cos(math.radians(self.incidents[i])))
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self.beam_tilted[i] = max(self.beam_tilted[i], 0)
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self.diffuse_tilted.append(self.diffuse_horizontal[i] * ((1 + math.cos(math.radians(self.tilt_angle))) / 2))
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self.total_radiation_tilted.append(self.beam_tilted[i] + self.diffuse_tilted[i])
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self.df['beam tilted (Wh/m2)'] = self.beam_tilted
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self.df['diffuse tilted (Wh/m2)'] = self.diffuse_tilted
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self.df['total radiation tilted (Wh/m2)'] = self.total_radiation_tilted
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def calculate(self) -> pd.DataFrame:
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self.dni_extra()
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self.clearness_index()
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self.diffuse_fraction()
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self.radiation_components_horizontal()
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self.radiation_components_tilted()
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return self.df
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def enrich(self):
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tilted_radiation = self.total_radiation_tilted
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self.building.roofs[0].global_irradiance_tilted[cte.HOUR] = [x * cte.WATTS_HOUR_TO_JULES for x in
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tilted_radiation]
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self.building.roofs[0].global_irradiance_tilted[cte.HOUR] = [x * cte.WATTS_HOUR_TO_JULES for x in
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tilted_radiation]
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self.building.roofs[0].global_irradiance_tilted[cte.MONTH] = (
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MonthlyValues.get_total_month(self.building.roofs[0].global_irradiance_tilted[cte.HOUR]))
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self.building.roofs[0].global_irradiance_tilted[cte.YEAR] = \
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[sum(self.building.roofs[0].global_irradiance_tilted[cte.MONTH])]
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146
scripts/solar_angles.py
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146
scripts/solar_angles.py
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@ -0,0 +1,146 @@
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import math
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import pandas as pd
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from datetime import datetime
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from pathlib import Path
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class CitySolarAngles:
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def __init__(self, file_name, location_latitude, location_longitude, tilt_angle, surface_azimuth_angle,
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standard_meridian=-75):
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self.file_name = file_name
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self.location_latitude = location_latitude
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self.location_longitude = location_longitude
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self.location_latitude_rad = math.radians(location_latitude)
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self.surface_azimuth_angle = surface_azimuth_angle
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self.surface_azimuth_rad = math.radians(surface_azimuth_angle)
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self.tilt_angle = tilt_angle
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self.tilt_angle_rad = math.radians(tilt_angle)
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self.standard_meridian = standard_meridian
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self.longitude_correction = (location_longitude - standard_meridian) * 4
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self.timezone = 'Etc/GMT+5'
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self.eot = []
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self.ast = []
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self.hour_angles = []
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self.declinations = []
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self.solar_altitudes = []
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self.solar_azimuths = []
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self.zeniths = []
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self.incidents = []
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self.beam_tilted = []
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self.factor = []
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self.times = pd.date_range(start='2023-01-01', end='2024-01-01', freq='H', tz=self.timezone)
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self.df = pd.DataFrame(index=self.times)
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self.day_of_year = self.df.index.dayofyear
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def solar_time(self, datetime_val, day_of_year):
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b = (day_of_year - 81) * 2 * math.pi / 364
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eot = 9.87 * math.sin(2 * b) - 7.53 * math.cos(b) - 1.5 * math.sin(b)
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self.eot.append(eot)
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# Calculate Local Solar Time (LST)
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lst_hour = datetime_val.hour
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lst_minute = datetime_val.minute
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lst_second = datetime_val.second
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lst = lst_hour + lst_minute / 60 + lst_second / 3600
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# Calculate Apparent Solar Time (AST) in decimal hours
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ast_decimal = lst + eot / 60 + self.longitude_correction / 60
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ast_hours = int(ast_decimal)
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ast_minutes = round((ast_decimal - ast_hours) * 60)
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# Ensure ast_minutes is within valid range
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if ast_minutes == 60:
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ast_hours += 1
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ast_minutes = 0
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elif ast_minutes < 0:
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ast_minutes = 0
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ast_time = datetime(year=datetime_val.year, month=datetime_val.month, day=datetime_val.day,
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hour=ast_hours, minute=ast_minutes)
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self.ast.append(ast_time)
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return ast_time
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def declination_angle(self, day_of_year):
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declination = 23.45 * math.sin(math.radians(360 / 365 * (284 + day_of_year)))
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declination_radian = math.radians(declination)
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self.declinations.append(declination)
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return declination_radian
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def hour_angle(self, ast_time):
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hour_angle = ((ast_time.hour * 60 + ast_time.minute) - 720) / 4
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hour_angle_radian = math.radians(hour_angle)
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self.hour_angles.append(hour_angle)
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return hour_angle_radian
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def solar_altitude(self, declination_radian, hour_angle_radian):
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solar_altitude_radians = math.asin(math.cos(self.location_latitude_rad) * math.cos(declination_radian) *
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math.cos(hour_angle_radian) + math.sin(self.location_latitude_rad) *
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math.sin(declination_radian))
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solar_altitude = math.degrees(solar_altitude_radians)
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self.solar_altitudes.append(solar_altitude)
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return solar_altitude_radians
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def zenith(self, solar_altitude_radians):
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solar_altitude = math.degrees(solar_altitude_radians)
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zenith_degree = 90 - solar_altitude
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zenith_radian = math.radians(zenith_degree)
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self.zeniths.append(zenith_degree)
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return zenith_radian
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def solar_azimuth_analytical(self, hourangle, declination, zenith):
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numer = (math.cos(zenith) * math.sin(self.location_latitude_rad) - math.sin(declination))
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denom = (math.sin(zenith) * math.cos(self.location_latitude_rad))
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if math.isclose(denom, 0.0, abs_tol=1e-8):
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cos_azi = 1.0
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else:
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cos_azi = numer / denom
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cos_azi = max(-1.0, min(1.0, cos_azi))
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sign_ha = math.copysign(1, hourangle)
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solar_azimuth_radians = sign_ha * math.acos(cos_azi) + math.pi
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solar_azimuth_degrees = math.degrees(solar_azimuth_radians)
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self.solar_azimuths.append(solar_azimuth_degrees)
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return solar_azimuth_radians
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def incident_angle(self, solar_altitude_radians, solar_azimuth_radians):
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incident_radian = math.acos(math.cos(solar_altitude_radians) *
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math.cos(abs(solar_azimuth_radians - self.surface_azimuth_rad)) *
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math.sin(self.tilt_angle_rad) + math.sin(solar_altitude_radians) *
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math.cos(self.tilt_angle_rad))
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incident_angle_degrees = math.degrees(incident_radian)
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self.incidents.append(incident_angle_degrees)
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return incident_radian
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@property
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def calculate(self) -> pd.DataFrame:
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for i in range(len(self.times)):
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datetime_val = self.times[i]
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day_of_year = self.day_of_year[i]
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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
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
1
selected_buildings.csv
Normal file
1
selected_buildings.csv
Normal file
|
@ -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
|
|
59
solar_radiation.py
Normal file
59
solar_radiation.py
Normal file
|
@ -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')
|
8761
solar_radiation_horizontal_selected_buildings.csv
Normal file
8761
solar_radiation_horizontal_selected_buildings.csv
Normal file
File diff suppressed because it is too large
Load Diff
8761
solar_radiation_tilted_selected_buildings.csv
Normal file
8761
solar_radiation_tilted_selected_buildings.csv
Normal file
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue
Block a user