def cloud_cover_to_irradiance_campbell_norman(self, cloud_cover, **kwargs):
"""
Estimates irradiance from cloud cover in the following steps:
1. Determine transmittance using a function of cloud cover e.g.
:py:meth:`~ForecastModel.cloud_cover_to_transmittance_linear`
2. Calculate GHI, DNI, DHI using the
:py:func:`pvlib.irradiance.campbell_norman` model
Parameters
----------
cloud_cover : Series
Returns
-------
irradiance : DataFrame
Columns include ghi, dni, dhi
"""
# in principle, get_solarposition could use the forecast
# pressure, temp, etc., but the cloud cover forecast is not
# accurate enough to justify using these minor corrections
solar_position = self.location.get_solarposition(cloud_cover.index)
dni_extra = get_extra_radiation(cloud_cover.index)
transmittance = self.cloud_cover_to_transmittance_linear(
cloud_cover, **kwargs)
irrads = campbell_norman(solar_position['apparent_zenith'],
transmittance,
dni_extra=dni_extra)
irrads = irrads.fillna(0)
return irrads
def test_campbell_norman():
expected = pd.DataFrame(np.array(
[[863.859736967, 653.123094076, 220.65905025]]),
columns=['ghi', 'dni', 'dhi'],
index=[0])
out = irradiance.campbell_norman(pd.Series([10]),
pd.Series([0.5]),
pd.Series([109764.21013135818]),
dni_extra=1400)
assert_frame_equal(out, expected)
def calculate_overcast_spectrl2():
"""
This example will loop over a range of cloud covers and latitudes
(at longitude=0) for a specific date and calculate the spectral
irradiance with and without accounting for clouds. Clouds are accounted for
by applying the cloud opacity factor defined in [1]. Several steps
are required:
1. Calculate the atmospheric and solar conditions for the
location and time
2. Calculate the spectral irradiance using `pvlib.spectrum.spectrl2`
for clear sky conditions
3. Calculate the dni, dhi, and ghi for cloudy conditions using
`pvlib.irradiance.campbell_norman`
4. Determine total in-plane irradiance and its beam,
sky diffuse and ground
reflected components for cloudy conditions -
`pvlib.irradiance.get_total_irradiance`
5. Calculate the dni, dhi, and ghi for clear sky conditions
using `pvlib.irradiance.campbell_norman`
6. Determine total in-plane irradiance and its beam,
sky diffuse and ground
reflected components for clear sky conditions -
`pvlib.irradiance.get_total_irradiance`
7. Calculate the cloud opacity factor [1] and scale the
spectral results from step 4 - func cloud_opacity_factor
8. Plot the results - func plot_spectral_irradiance
"""
month = 2
hour_of_day = 12
altitude = 0.0
longitude = 0.0
latitudes = [10, 40]
# cloud cover in fraction units
cloud_covers = [0.2, 0.5]
water_vapor_content = 0.5
tau500 = 0.1
ground_albedo = 0.06
ozone = 0.3
surface_tilt = 0.0
ctime, pv_system = setup_pv_system(month, hour_of_day)
for cloud_cover in cloud_covers:
for latitude in latitudes:
sol = get_solarposition(ctime, latitude, longitude)
az = sol['apparent_zenith'].to_numpy()
airmass_relative = get_relative_airmass(az,
model='kastenyoung1989')
pressure = pvlib.atmosphere.alt2pres(altitude)
az = sol['apparent_zenith'].to_numpy()
azimuth = sol['azimuth'].to_numpy()
surface_azimuth = pv_system['surface_azimuth']
transmittance = (1.0 - cloud_cover) * 0.75
calc_aoi = aoi(surface_tilt, surface_azimuth, az, azimuth)
# day of year is an int64index array so access first item
day_of_year = ctime.dayofyear[0]
spectra = pvlib.spectrum.spectrl2(
apparent_zenith=az,
aoi=calc_aoi,
surface_tilt=surface_tilt,
ground_albedo=ground_albedo,
surface_pressure=pressure,
relative_airmass=airmass_relative,
precipitable_water=water_vapor_content,
ozone=ozone,
aerosol_turbidity_500nm=tau500,
dayofyear=day_of_year)
irrads_clouds = campbell_norman(sol['zenith'].to_numpy(),
transmittance)
# Convert the irradiance to a plane with tilt zero
# horizontal to the earth. This is done applying
# tilt=0 to POA calculations using the output from
# `campbell_norman`. The POA calculations include
# calculating sky and ground diffuse light where
# specific models can be selected (we use default).
poa_irr_clouds = get_total_irradiance(
surface_tilt=surface_tilt,
surface_azimuth=pv_system['surface_azimuth'],
dni=irrads_clouds['dni'],
ghi=irrads_clouds['ghi'],
dhi=irrads_clouds['dhi'],
solar_zenith=sol['apparent_zenith'],
solar_azimuth=sol['azimuth'])
show_info(latitude, poa_irr_clouds)
zen = sol['zenith'].to_numpy()
irr_clearsky = campbell_norman(zen, transmittance=0.75)
poa_irr_clearsky = get_total_irradiance(
surface_tilt=surface_tilt,
surface_azimuth=pv_system['surface_azimuth'],
dni=irr_clearsky['dni'],
ghi=irr_clearsky['ghi'],
dhi=irr_clearsky['dhi'],
solar_zenith=sol['apparent_zenith'],
solar_azimuth=sol['azimuth'])
show_info(latitude, poa_irr_clearsky)
poa_dr = poa_irr_clouds['poa_direct'].values
poa_diff = poa_irr_clouds['poa_diffuse'].values
poa_global = poa_irr_clouds['poa_global'].values
f_dir, f_diff = cloud_opacity_factor(poa_dr, poa_diff, poa_global,
spectra)
plot_spectral_irr(spectra,
f_dir,
f_diff,
lat=latitude,
doy=day_of_year,
year=ctime.year[0],
clouds=cloud_cover)