def apply_decomposition_model(weather_df, model, location):
"""
Uses the specified decomposition model to calculate DNI and DHI.
Parameters
----------
weather_df : :pandas:`DataFrame`
Weather DataFrame containing all variables needed to apply the
decomposition model. See model functions for more information.
model : :obj:`str`
Decomposition model to use. Choose from 'reindl', 'erbs' or 'disc'.
location : :pvlib:`Location`
Returns
-------
:pandas:`DataFrame`
DataFrame with DNI and DHI.
"""
solar_position = location.get_solarposition(
weather_df.index, pressure=weather_df['pressure'].mean(),
temperature=weather_df['temp_air'].mean())
if model == 'reindl':
solar_position = location.get_solarposition(
weather_df.index, pressure=weather_df['pressure'].mean(),
temperature=weather_df['temp_air'].mean())
df = reindl(weather_df.ghi, weather_df.i0_h, solar_position.elevation)
df['dni_corrected'] = irradiance.dni(
weather_df['ghi'], df['dhi'], solar_position.zenith,
clearsky_dni=location.get_clearsky(
weather_df.index, solar_position=solar_position).dni,
clearsky_tolerance=1.1,
zenith_threshold_for_zero_dni=88.0,
zenith_threshold_for_clearsky_limit=80.0)
elif model == 'erbs':
df = irradiance.erbs(weather_df.ghi, solar_position.zenith,
weather_df.index)
df['dni_corrected'] = irradiance.dni(
weather_df['ghi'], df['dhi'], solar_position.zenith,
clearsky_dni=location.get_clearsky(
weather_df.index, solar_position=solar_position).dni,
clearsky_tolerance=1.1,
zenith_threshold_for_zero_dni=88.0,
zenith_threshold_for_clearsky_limit=80.0)
elif model == 'disc':
df = irradiance.disc(weather_df.ghi, solar_position.zenith,
weather_df.index, weather_df.pressure.mean())
df['dhi'] = weather_df.ghi - df.dni * pvlib.tools.cosd(
solar_position.zenith)
df['gni'] = df.dni + df.dhi
return df
def test_erbs_min_cos_zenith_max_zenith():
# map out behavior under difficult conditions with various
# limiting kwargs settings
columns = ['dni', 'dhi', 'kt']
times = pd.DatetimeIndex(['2016-07-19 06:11:00'], tz='America/Phoenix')
# max_zenith keeps these results reasonable
out = irradiance.erbs(ghi=1.0,
zenith=89.99999,
datetime_or_doy=times,
min_cos_zenith=0)
expected = pd.DataFrame(np.array([[0., 1., 1.]]),
columns=columns,
index=times)
assert_frame_equal(out, expected)
# 4-5 9s will produce bad behavior without max_zenith limit
out = irradiance.erbs(ghi=1.0,
zenith=89.99999,
datetime_or_doy=times,
max_zenith=100)
expected = pd.DataFrame(np.array(
[[6.00115286e+03, 9.98952601e-01, 1.16377640e-02]]),
columns=columns,
index=times)
assert_frame_equal(out, expected)
# 1-2 9s will produce bad behavior without either limit
out = irradiance.erbs(ghi=1.0,
zenith=89.99,
datetime_or_doy=times,
min_cos_zenith=0,
max_zenith=100)
expected = pd.DataFrame(np.array(
[[4.78419761e+03, 1.65000000e-01, 1.00000000e+00]]),
columns=columns,
index=times)
assert_frame_equal(out, expected)
# check default behavior under hardest condition
out = irradiance.erbs(ghi=1.0, zenith=90, datetime_or_doy=times)
expected = pd.DataFrame(np.array([[0., 1., 0.01163776]]),
columns=columns,
index=times)
assert_frame_equal(out, expected)
def test_erbs_all_scalar():
ghi = 1000
zenith = 10
doy = 180
expected = pd.DataFrame(np.
array([[ 8.42358014e+02, 1.70439297e+02, 7.68919470e-01]]),
columns=['dni', 'dhi', 'kt'])
out = irradiance.erbs(ghi, zenith, doy)
assert_frame_equal(out, expected)
def test_erbs_all_scalar():
ghi = 1000
zenith = 10
doy = 180
expected = pd.DataFrame(np.array(
[[8.42358014e+02, 1.70439297e+02, 7.68919470e-01]]),
columns=['dni', 'dhi', 'kt'])
out = irradiance.erbs(ghi, zenith, doy)
assert_frame_equal(out, expected)
def dhi(self, time, lat, lon, ghi):
dhi = []
try:
ghi = self.ghi(ghi)
zenith = self.zenith(time, lat, lon)
dhi = irradiance.erbs(ghi, zenith, time)['dhi']
except Exception as e:
f = sys.exc_info()[2].tb_frame.f_back
print("error: %s, line %s, in %s" %
(f.f_code.co_filename, str(f.f_lineno), f.f_code.co_name))
print(e)
return (dhi)
def calc_dhi_erbs(self, time_range, ghi):
"""
Compute the dhi-value based on given ghi value within a time range using
the ERBS algortihm.
Parameter:
==========
time_range: pandas Series - time range.
ghi: pandas Series - with global horizontal irradiance (ghi) >> taken from DWD Forecast. [W/m2]
"""
dhi = erbs(ghi=ghi, zenith=self.solpos.zenith, datetime_or_doy=time_range)
return dhi
def test_erbs_all_scalar():
ghi = 1000
zenith = 10
doy = 180
expected = OrderedDict()
expected['dni'] = 8.42358014e+02
expected['dhi'] = 1.70439297e+02
expected['kt'] = 7.68919470e-01
out = irradiance.erbs(ghi, zenith, doy)
for k, v in out.items():
assert_allclose(v, expected[k], 5)
def test_erbs():
ghi = pd.Series([0, 50, 1000, 1000])
zenith = pd.Series([120, 85, 10, 10])
doy = pd.Series([1, 1, 1, 180])
expected = pd.DataFrame(np.
array([[ -0.00000000e+00, 0.00000000e+00, -0.00000000e+00],
[ 9.67127061e+01, 4.15709323e+01, 4.05715990e-01],
[ 7.94187742e+02, 2.17877755e+02, 7.18119416e-01],
[ 8.42358014e+02, 1.70439297e+02, 7.68919470e-01]]),
columns=['dni', 'dhi', 'kt'])
out = irradiance.erbs(ghi, zenith, doy)
assert_frame_equal(np.round(out, 0), np.round(expected, 0))
def test_erbs_all_scalar():
ghi = 1000
zenith = 10
doy = 180
expected = OrderedDict()
expected['dni'] = 8.42358014e+02
expected['dhi'] = 1.70439297e+02
expected['kt'] = 7.68919470e-01
out = irradiance.erbs(ghi, zenith, doy)
for k, v in out.items():
assert_allclose(v, expected[k], 5)
def test_erbs():
ghi = pd.Series([0, 50, 1000, 1000])
zenith = pd.Series([120, 85, 10, 10])
doy = pd.Series([1, 1, 1, 180])
expected = pd.DataFrame(np.array(
[[-0.00000000e+00, 0.00000000e+00, -0.00000000e+00],
[9.67127061e+01, 4.15709323e+01, 4.05715990e-01],
[7.94187742e+02, 2.17877755e+02, 7.18119416e-01],
[8.42358014e+02, 1.70439297e+02, 7.68919470e-01]]),
columns=['dni', 'dhi', 'kt'])
out = irradiance.erbs(ghi, zenith, doy)
assert_frame_equal(np.round(out, 0), np.round(expected, 0))
def test_erbs():
index = pd.DatetimeIndex(['20190101'] * 3 + ['20190620'])
ghi = pd.Series([0, 50, 1000, 1000], index=index)
zenith = pd.Series([120, 85, 10, 10], index=index)
expected = pd.DataFrame(np.array(
[[0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[9.67192672e+01, 4.15703604e+01, 4.05723511e-01],
[7.94205651e+02, 2.17860117e+02, 7.18132729e-01],
[8.42001578e+02, 1.70790318e+02, 7.68214312e-01]]),
columns=['dni', 'dhi', 'kt'],
index=index)
out = irradiance.erbs(ghi, zenith, index)
assert_frame_equal(np.round(out, 0), np.round(expected, 0))
def ghi_from_obstruction(GHI, sza, obstr, SVF, doy):
"""
Description of ghi_from_obstruction
Returns solar irradiance value for a location
Args:
GHI (undefined): Global Horizontal Irradiance
sza (undefined): Solar Zenith Angle in degrees
obstr (undefined): Obstruction information calculated with get_obstruction method
SVF (undefined): Sky View Factor
doy (undefined): Day of the year
"""
sol = erbs(GHI, sza, doy)
if obstr != 0:
obstr = 0
else:
obstr = 1
return sol['dni'] * np.cos(np.deg2rad(sza)) * obstr + SVF * sol['dhi']
def time_erbs(self):
irradiance.erbs(self.clearsky_irradiance.ghi,
self.solar_position.apparent_zenith,
self.times)
