def get_poa(self,
dates,
ghi,
dhi,
dni,
sun_zenith,
sun_azimuth,
dni_extra=None,
airmass=None,
model="haydavies"):
if self.method == "pvlib":
sun_zenith = deg(sun_zenith)
sun_azimuth = deg(sun_azimuth)
if dni_extra is None:
dni_extra = irradiance.extraradiation(DatetimeIndex(dates))
if airmass is None:
airmass = atmosphere.relativeairmass(sun_zenith)
poa = irradiance.total_irrad(self.tilt,
self.azimuth,
sun_zenith,
sun_azimuth,
dni,
ghi,
dhi,
dni_extra=dni_extra,
airmass=airmass,
model=model,
albedo=self.albedo)
self.poa = poa['poa_global']
def test_perez():
AM = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
irradiance.perez(40, 180, irrad_data['DHI'], irrad_data['DNI'],
dni_et,
ephem_data['apparent_zenith'],
ephem_data['apparent_azimuth'],
AM)
def test_perez_components():
am = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
dni = irrad_data['dni'].copy()
dni.iloc[2] = np.nan
out, df_components = irradiance.perez(40, 180, irrad_data['dhi'], dni,
dni_et, ephem_data['apparent_zenith'],
ephem_data['azimuth'], am, return_components=True)
expected = pd.Series(np.array(
[ 0. , 31.46046871, np.nan, 45.45539877]),
index=times)
expected_components = pd.DataFrame(
np.array([[ 0. , 26.84138589, np.nan, 31.72696071],
[ 0. , 0. , np.nan, 4.47966439],
[ 0. , 4.62212181, np.nan, 9.25316454]]).T,
columns=['isotropic', 'circumsolar', 'horizon'],
index=times
)
if pandas_0_22():
expected_for_sum = expected.copy()
expected_for_sum.iloc[2] = 0
else:
expected_for_sum = expected
sum_components = df_components.sum(axis=1)
assert_series_equal(out, expected, check_less_precise=2)
assert_frame_equal(df_components, expected_components)
assert_series_equal(sum_components, expected_for_sum, check_less_precise=2)
def test_perez_components():
am = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
dni = irrad_data['dni'].copy()
dni.iloc[2] = np.nan
out, df_components = irradiance.perez(40,
180,
irrad_data['dhi'],
dni,
dni_et,
ephem_data['apparent_zenith'],
ephem_data['azimuth'],
am,
return_components=True)
expected = pd.Series(np.array([0., 31.46046871, np.nan, 45.45539877]),
index=times)
expected_components = pd.DataFrame(
np.array([[0., 26.84138589, np.nan, 31.72696071],
[0., 0., np.nan, 4.47966439],
[0., 4.62212181, np.nan, 9.25316454]]).T,
columns=['isotropic', 'circumsolar', 'horizon'],
index=times)
sum_components = df_components.sum(axis=1)
assert_series_equal(out, expected, check_less_precise=2)
assert_frame_equal(df_components, expected_components)
assert_series_equal(sum_components, expected, check_less_precise=2)
def test_perez_arrays():
am = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
dni = irrad_data['dni'].copy()
dni.iloc[2] = np.nan
out = irradiance.perez(40, 180, irrad_data['dhi'].values, dni.values,
dni_et, ephem_data['apparent_zenith'].values,
ephem_data['azimuth'].values, am.values)
expected = np.array([0., 31.46046871, np.nan, 45.45539877])
assert_allclose(out, expected, atol=1e-2)
def test_perez_arrays():
am = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
dni = irrad_data['dni'].copy()
dni.iloc[2] = np.nan
out = irradiance.perez(40, 180, irrad_data['dhi'].values, dni.values,
dni_et, ephem_data['apparent_zenith'].values,
ephem_data['azimuth'].values, am.values)
expected = np.array(
[ 0. , 31.46046871, np.nan, 45.45539877])
assert_allclose(out, expected, atol=1e-2)
def test_perez():
am = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
dni = irrad_data['dni'].copy()
dni.iloc[2] = np.nan
out = irradiance.perez(40, 180, irrad_data['dhi'], dni, dni_et,
ephem_data['apparent_zenith'],
ephem_data['azimuth'], am)
expected = pd.Series(np.array([0., 31.46046871, np.nan, 45.45539877]),
index=times)
assert_series_equal(out, expected, check_less_precise=2)
def test_globalinplane():
aoi = irradiance.aoi(40, 180, ephem_data['apparent_zenith'],
ephem_data['apparent_azimuth'])
airmass = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
gr_sand = irradiance.grounddiffuse(40, ghi, surface_type='sand')
diff_perez = irradiance.perez(
40, 180, irrad_data['dhi'], irrad_data['dni'], dni_et,
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'], airmass)
irradiance.globalinplane(
aoi=aoi, dni=irrad_data['dni'], poa_sky_diffuse=diff_perez,
poa_ground_diffuse=gr_sand)
def test_globalinplane():
aoi = irradiance.aoi(40, 180, ephem_data['apparent_zenith'],
ephem_data['azimuth'])
airmass = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
gr_sand = irradiance.grounddiffuse(40, ghi, surface_type='sand')
diff_perez = irradiance.perez(
40, 180, irrad_data['dhi'], irrad_data['dni'], dni_et,
ephem_data['apparent_zenith'], ephem_data['azimuth'], airmass)
irradiance.globalinplane(
aoi=aoi, dni=irrad_data['dni'], poa_sky_diffuse=diff_perez,
poa_ground_diffuse=gr_sand)
def test_globalinplane():
AOI = irradiance.aoi(40, 180, ephem_data['apparent_zenith'],
ephem_data['apparent_azimuth'])
AM = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
gr_sand = irradiance.grounddiffuse(40, ghi, surface_type='sand')
diff_perez = irradiance.perez(
40, 180, irrad_data['DHI'], irrad_data['DNI'], dni_et,
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'], AM)
irradiance.globalinplane(
AOI=AOI, DNI=irrad_data['DNI'], In_Plane_SkyDiffuse=diff_perez,
GR=gr_sand)
def test_perez():
am = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
dni = irrad_data['dni'].copy()
dni.iloc[2] = np.nan
out = irradiance.perez(40, 180, irrad_data['dhi'], dni,
dni_et, ephem_data['apparent_zenith'],
ephem_data['azimuth'], am)
expected = pd.Series(np.array(
[ 0. , 31.46046871, np.nan, 45.45539877]),
index=times)
assert_series_equal(out, expected, check_less_precise=2)
def test_total_irrad():
models = ['isotropic', 'klutcher', 'haydavies', 'reindl', 'king', 'perez']
AM = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
for model in models:
total = irradiance.total_irrad(
32, 180,
ephem_data['apparent_zenith'], ephem_data['azimuth'],
dni=irrad_data['dni'], ghi=irrad_data['ghi'], dhi=irrad_data['dhi'],
dni_extra=dni_et, airmass=AM,
model=model,
surface_type='urban')
def test_globalinplane():
AOI = irradiance.aoi(40, 180, ephem_data['apparent_zenith'],
ephem_data['apparent_azimuth'])
AM = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
gr_sand = irradiance.grounddiffuse(40, ghi, surface_type='sand')
diff_perez = irradiance.perez(40, 180, irrad_data['DHI'],
irrad_data['DNI'], dni_et,
ephem_data['apparent_zenith'],
ephem_data['apparent_azimuth'], AM)
irradiance.globalinplane(AOI=AOI,
DNI=irrad_data['DNI'],
In_Plane_SkyDiffuse=diff_perez,
GR=gr_sand)
def get_perfect_voltage_for_a_day(start, freq):
"""This method is used to build a pandas serie with voltage values.
This serie has DateTime index and contains a value for every "freq"
seconds during 24 hours starting from "start" date.
There are several assumptions:
1. Location is Munich
2. A battery is pointing to the south, amount of blocks is 20
3. Sandia Module database is used
4. pvlib library is heavily used
:param start: datetime. First timestamp in result series
:param freq: str. How often voltage should be sampled
:return: voltage : Series
"""
surface_tilt = _munich_location.latitude
surface_azimuth = 180 # pointing south
date_range = pd.date_range(start=start,
end=start + dt.timedelta(
hours=23, minutes=59, seconds=59),
freq=freq, tz=_munich_location.tz)
clearsky_estimations = _munich_location.get_clearsky(date_range)
dni_extra = irradiance.extraradiation(date_range)
solar_position = solarposition.get_solarposition(
date_range, _munich_location.latitude, _munich_location.longitude)
airmass = atmosphere.relativeairmass(solar_position['apparent_zenith'])
pressure = atmosphere.alt2pres(_munich_location.altitude)
am_abs = atmosphere.absoluteairmass(airmass, pressure)
total_irrad = irradiance.total_irrad(surface_tilt,
surface_azimuth,
solar_position['apparent_zenith'],
solar_position['azimuth'],
clearsky_estimations['dni'],
clearsky_estimations['ghi'],
clearsky_estimations['dhi'],
dni_extra=dni_extra,
model='haydavies')
temps = pvsystem.sapm_celltemp(total_irrad['poa_global'], 0, 15)
aoi = irradiance.aoi(surface_tilt, surface_azimuth,
solar_position['apparent_zenith'],
solar_position['azimuth'])
# add 0.0001 to avoid np.log(0) and warnings about that
effective_irradiance = pvsystem.sapm_effective_irradiance(
total_irrad['poa_direct'], total_irrad['poa_diffuse'], am_abs,
aoi, _sandia_module) + 0.0001
sapm = pvsystem.sapm(effective_irradiance, temps['temp_cell'],
_sandia_module)
return sapm['p_mp'] * _module_count
def DC_out(solpos, poa_irrad, aoi, pvtemps):
## First calculate airmass
airmass = atmosphere.relativeairmass(solpos['apparent_zenith'])
## Get list of modules
sandia_modules = pvsystem.retrieve_sam('SandiaMod')
## Choose model (e.g. CS5P_220M___2009)
sandia_module = sandia_modules.Canadian_Solar_CS5P_220M___2009_
## Run SAPM model
effective_irradiance = pvsystem.sapm_effective_irradiance(
poa_irrad.poa_direct, poa_irrad.poa_diffuse, airmass, aoi,
sandia_module)
p_dc = pvsystem.sapm(effective_irradiance, pvtemps['temp_cell'],
sandia_module)
return (p_dc)
def test_total_irrad():
models = ['isotropic', 'klutcher', 'haydavies', 'reindl', 'king', 'perez']
AM = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
for model in models:
total = irradiance.total_irrad(32,
180,
ephem_data['apparent_zenith'],
ephem_data['azimuth'],
dni=irrad_data['dni'],
ghi=irrad_data['ghi'],
dhi=irrad_data['dhi'],
dni_extra=dni_et,
airmass=AM,
model=model,
surface_type='urban')
def get_airmass(self,
times=None,
solar_position=None,
model='kastenyoung1989'):
"""
Calculate the relative and absolute airmass.
Automatically chooses zenith or apparant zenith
depending on the selected model.
Parameters
----------
times : None or DatetimeIndex
Only used if solar_position is not provided.
solar_position : None or DataFrame
DataFrame with with columns 'apparent_zenith', 'zenith'.
model : str
Relative airmass model
Returns
-------
airmass : DataFrame
Columns are 'airmass_relative', 'airmass_absolute'
"""
if solar_position is None:
solar_position = self.get_solarposition(times)
if model in atmosphere.APPARENT_ZENITH_MODELS:
zenith = solar_position['apparent_zenith']
elif model in atmosphere.TRUE_ZENITH_MODELS:
zenith = solar_position['zenith']
else:
raise ValueError('{} is not a valid airmass model'.format(model))
airmass_relative = atmosphere.relativeairmass(zenith, model)
pressure = atmosphere.alt2pres(self.altitude)
airmass_absolute = atmosphere.absoluteairmass(airmass_relative,
pressure)
airmass = pd.DataFrame()
airmass['airmass_relative'] = airmass_relative
airmass['airmass_absolute'] = airmass_absolute
return airmass
def get_airmass(self, times=None, solar_position=None,
model='kastenyoung1989'):
"""
Calculate the relative and absolute airmass.
Automatically chooses zenith or apparant zenith
depending on the selected model.
Parameters
----------
times : None or DatetimeIndex
Only used if solar_position is not provided.
solar_position : None or DataFrame
DataFrame with with columns 'apparent_zenith', 'zenith'.
model : str
Relative airmass model
Returns
-------
airmass : DataFrame
Columns are 'airmass_relative', 'airmass_absolute'
"""
if solar_position is None:
solar_position = self.get_solarposition(times)
if model in atmosphere.APPARENT_ZENITH_MODELS:
zenith = solar_position['apparent_zenith']
elif model in atmosphere.TRUE_ZENITH_MODELS:
zenith = solar_position['zenith']
else:
raise ValueError('{} is not a valid airmass model'.format(model))
airmass_relative = atmosphere.relativeairmass(zenith, model)
pressure = atmosphere.alt2pres(self.altitude)
airmass_absolute = atmosphere.absoluteairmass(airmass_relative,
pressure)
airmass = pd.DataFrame()
airmass['airmass_relative'] = airmass_relative
airmass['airmass_absolute'] = airmass_absolute
return airmass
def test_total_irrad():
models = ['isotropic', 'klutcher', 'klucher',
'haydavies', 'reindl', 'king', 'perez']
AM = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
for model in models:
total = irradiance.total_irrad(
32, 180,
ephem_data['apparent_zenith'], ephem_data['azimuth'],
dni=irrad_data['dni'], ghi=irrad_data['ghi'],
dhi=irrad_data['dhi'],
dni_extra=dni_et, airmass=AM,
model=model,
surface_type='urban')
assert total.columns.tolist() == ['poa_global', 'poa_direct',
'poa_diffuse', 'poa_sky_diffuse',
'poa_ground_diffuse']
def test_total_irrad():
models = ['isotropic', 'klutcher', 'klucher',
'haydavies', 'reindl', 'king', 'perez']
AM = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
for model in models:
total = irradiance.total_irrad(
32, 180,
ephem_data['apparent_zenith'], ephem_data['azimuth'],
dni=irrad_data['dni'], ghi=irrad_data['ghi'],
dhi=irrad_data['dhi'],
dni_extra=dni_et, airmass=AM,
model=model,
surface_type='urban')
assert total.columns.tolist() == ['poa_global', 'poa_direct',
'poa_diffuse', 'poa_sky_diffuse',
'poa_ground_diffuse']
def test_ineichen_series():
tus = Location(32.2, -111, 'US/Arizona', 700)
times = pd.date_range(start='2014-06-24', end='2014-06-25', freq='3h')
times_localized = times.tz_localize(tus.tz)
ephem_data = solarposition.get_solarposition(times_localized, tus.latitude,
tus.longitude)
am = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
am = atmosphere.absoluteairmass(am, atmosphere.alt2pres(tus.altitude))
expected = pd.DataFrame(
np.array([[0., 0., 0.], [0., 0., 0.],
[91.12492792, 321.16092181, 51.17628184],
[716.46580533, 888.90147035, 99.5050056],
[1053.42066043, 953.24925854, 116.32868969],
[863.54692781, 922.06124712, 106.95536561],
[271.06382274, 655.44925241, 73.05968071], [0., 0., 0.],
[0., 0., 0.]]),
columns=['ghi', 'dni', 'dhi'],
index=times_localized)
out = clearsky.ineichen(ephem_data['apparent_zenith'], am, 3)
assert_frame_equal(expected, out)
def test_ineichen_series():
tus = Location(32.2, -111, 'US/Arizona', 700)
times = pd.date_range(start='2014-06-24', end='2014-06-25', freq='3h')
times_localized = times.tz_localize(tus.tz)
ephem_data = solarposition.get_solarposition(times_localized, tus.latitude,
tus.longitude)
am = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
am = atmosphere.absoluteairmass(am, atmosphere.alt2pres(tus.altitude))
expected = pd.DataFrame(np.
array([[ 0. , 0. , 0. ],
[ 0. , 0. , 0. ],
[ 91.12492792, 321.16092181, 51.17628184],
[ 716.46580533, 888.90147035, 99.5050056 ],
[ 1053.42066043, 953.24925854, 116.32868969],
[ 863.54692781, 922.06124712, 106.95536561],
[ 271.06382274, 655.44925241, 73.05968071],
[ 0. , 0. , 0. ],
[ 0. , 0. , 0. ]]),
columns=['ghi', 'dni', 'dhi'],
index=times_localized)
out = clearsky.ineichen(ephem_data['apparent_zenith'], am, 3)
assert_frame_equal(expected, out)
def test_airmass_invalid():
atmosphere.relativeairmass(ephem_data['zenith'], 'invalid')
def test_airmass_invalid():
atmosphere.relativeairmass(ephem_data['zenith'], 'invalid')
def test_perez():
AM = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
irradiance.perez(40, 180, irrad_data['dhi'], irrad_data['dni'], dni_et,
ephem_data['apparent_zenith'],
ephem_data['apparent_azimuth'], AM)
def test_airmass_invalid():
with pytest.raises(ValueError):
atmosphere.relativeairmass(ephem_data['zenith'], 'invalid')
def test_airmass(model):
out = atmosphere.relativeairmass(ephem_data['zenith'], model)
assert isinstance(out, pd.Series)
out = atmosphere.relativeairmass(ephem_data['zenith'].values, model)
assert isinstance(out, np.ndarray)
def get_irradiance(self,
dni,
ghi,
dhi,
dni_extra=None,
airmass=None,
model='haydavies',
**kwargs):
"""
Uses the :func:`irradiance.total_irrad` function to calculate
the plane of array irradiance components on a tilted surface
defined by ``self.surface_tilt``, ``self.surface_azimuth``, and
``self.albedo``.
Parameters
----------
solar_zenith : float or Series.
Solar zenith angle.
solar_azimuth : float or Series.
Solar azimuth angle.
dni : float or Series
Direct Normal Irradiance
ghi : float or Series
Global horizontal irradiance
dhi : float or Series
Diffuse horizontal irradiance
dni_extra : float or Series
Extraterrestrial direct normal irradiance
airmass : float or Series
Airmass
model : String
Irradiance model.
**kwargs
Passed to :func:`irradiance.total_irrad`.
Returns
-------
poa_irradiance : DataFrame
Column names are: ``total, beam, sky, ground``.
"""
surface_tilt = kwargs.pop('surface_tilt', self.surface_tilt)
surface_azimuth = kwargs.pop('surface_azimuth', self.surface_azimuth)
try:
solar_zenith = kwargs['solar_zenith']
except KeyError:
solar_zenith = self.solar_zenith
try:
solar_azimuth = kwargs['solar_azimuth']
except KeyError:
solar_azimuth = self.solar_azimuth
# not needed for all models, but this is easier
if dni_extra is None:
dni_extra = irradiance.extraradiation(solar_zenith.index)
dni_extra = pd.Series(dni_extra, index=solar_zenith.index)
if airmass is None:
airmass = atmosphere.relativeairmass(solar_zenith)
return irradiance.total_irrad(surface_tilt,
surface_azimuth,
solar_zenith,
solar_azimuth,
dni,
ghi,
dhi,
dni_extra=dni_extra,
airmass=airmass,
model=model,
albedo=self.albedo,
**kwargs)
def test_bird():
"""Test Bird/Hulstrom Clearsky Model"""
times = pd.DatetimeIndex(start='1/1/2015 0:00', end='12/31/2015 23:00',
freq='H')
tz = -7 # test timezone
gmt_tz = pytz.timezone('Etc/GMT%+d' % -(tz))
times = times.tz_localize(gmt_tz) # set timezone
# match test data from BIRD_08_16_2012.xls
latitude = 40.
longitude = -105.
press_mB = 840.
o3_cm = 0.3
h2o_cm = 1.5
aod_500nm = 0.1
aod_380nm = 0.15
b_a = 0.85
alb = 0.2
eot = solarposition.equation_of_time_spencer71(times.dayofyear)
hour_angle = solarposition.hour_angle(times, longitude, eot) - 0.5 * 15.
declination = solarposition.declination_spencer71(times.dayofyear)
zenith = solarposition.solar_zenith_analytical(
np.deg2rad(latitude), np.deg2rad(hour_angle), declination
)
zenith = np.rad2deg(zenith)
airmass = atmosphere.relativeairmass(zenith, model='kasten1966')
etr = irradiance.extraradiation(times)
# test Bird with time series data
field_names = ('dni', 'direct_horizontal', 'ghi', 'dhi')
irrads = clearsky.bird(
zenith, airmass, aod_380nm, aod_500nm, h2o_cm, o3_cm, press_mB * 100.,
etr, b_a, alb
)
Eb, Ebh, Gh, Dh = (irrads[_] for _ in field_names)
clearsky_path = os.path.dirname(os.path.abspath(__file__))
pvlib_path = os.path.dirname(clearsky_path)
data_path = os.path.join(pvlib_path, 'data', 'BIRD_08_16_2012.csv')
testdata = pd.read_csv(data_path, usecols=range(1, 26), header=1).dropna()
testdata.index = times[1:48]
assert np.allclose(testdata['DEC'], np.rad2deg(declination[1:48]))
assert np.allclose(testdata['EQT'], eot[1:48], rtol=1e-4)
assert np.allclose(testdata['Hour Angle'], hour_angle[1:48])
assert np.allclose(testdata['Zenith Ang'], zenith[1:48])
dawn = zenith < 88.
dusk = testdata['Zenith Ang'] < 88.
am = pd.Series(np.where(dawn, airmass, 0.), index=times).fillna(0.0)
assert np.allclose(
testdata['Air Mass'].where(dusk, 0.), am[1:48], rtol=1e-3
)
direct_beam = pd.Series(np.where(dawn, Eb, 0.), index=times).fillna(0.)
assert np.allclose(
testdata['Direct Beam'].where(dusk, 0.), direct_beam[1:48], rtol=1e-3
)
direct_horz = pd.Series(np.where(dawn, Ebh, 0.), index=times).fillna(0.)
assert np.allclose(
testdata['Direct Hz'].where(dusk, 0.), direct_horz[1:48], rtol=1e-3
)
global_horz = pd.Series(np.where(dawn, Gh, 0.), index=times).fillna(0.)
assert np.allclose(
testdata['Global Hz'].where(dusk, 0.), global_horz[1:48], rtol=1e-3
)
diffuse_horz = pd.Series(np.where(dawn, Dh, 0.), index=times).fillna(0.)
assert np.allclose(
testdata['Dif Hz'].where(dusk, 0.), diffuse_horz[1:48], rtol=1e-3
)
# test keyword parameters
irrads2 = clearsky.bird(
zenith, airmass, aod_380nm, aod_500nm, h2o_cm, dni_extra=etr
)
Eb2, Ebh2, Gh2, Dh2 = (irrads2[_] for _ in field_names)
clearsky_path = os.path.dirname(os.path.abspath(__file__))
pvlib_path = os.path.dirname(clearsky_path)
data_path = os.path.join(pvlib_path, 'data', 'BIRD_08_16_2012_patm.csv')
testdata2 = pd.read_csv(data_path, usecols=range(1, 26), header=1).dropna()
testdata2.index = times[1:48]
direct_beam2 = pd.Series(np.where(dawn, Eb2, 0.), index=times).fillna(0.)
assert np.allclose(
testdata2['Direct Beam'].where(dusk, 0.), direct_beam2[1:48], rtol=1e-3
)
direct_horz2 = pd.Series(np.where(dawn, Ebh2, 0.), index=times).fillna(0.)
assert np.allclose(
testdata2['Direct Hz'].where(dusk, 0.), direct_horz2[1:48], rtol=1e-3
)
global_horz2 = pd.Series(np.where(dawn, Gh2, 0.), index=times).fillna(0.)
assert np.allclose(
testdata2['Global Hz'].where(dusk, 0.), global_horz2[1:48], rtol=1e-3
)
diffuse_horz2 = pd.Series(np.where(dawn, Dh2, 0.), index=times).fillna(0.)
assert np.allclose(
testdata2['Dif Hz'].where(dusk, 0.), diffuse_horz2[1:48], rtol=1e-3
)
# test scalars just at noon
# XXX: calculations start at 12am so noon is at index = 12
irrads3 = clearsky.bird(
zenith[12], airmass[12], aod_380nm, aod_500nm, h2o_cm, dni_extra=etr[12]
)
Eb3, Ebh3, Gh3, Dh3 = (irrads3[_] for _ in field_names)
# XXX: testdata starts at 1am so noon is at index = 11
np.allclose(
[Eb3, Ebh3, Gh3, Dh3],
testdata2[['Direct Beam', 'Direct Hz', 'Global Hz', 'Dif Hz']].iloc[11],
rtol=1e-3)
return pd.DataFrame({'Eb': Eb, 'Ebh': Ebh, 'Gh': Gh, 'Dh': Dh}, index=times)
def test_absoluteairmass():
relative_am = atmosphere.relativeairmass(ephem_data["zenith"], "simple")
atmosphere.absoluteairmass(relative_am)
atmosphere.absoluteairmass(relative_am, pressure=100000)
def test_airmass_invalid():
with pytest.raises(ValueError):
atmosphere.relativeairmass(ephem_data['zenith'], 'invalid')
def test_perez():
am = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
out = irradiance.perez(40, 180, irrad_data['dhi'], irrad_data['dni'],
dni_et, ephem_data['apparent_zenith'],
ephem_data['azimuth'], am)
assert not out.isnull().any()
def test_perez():
am = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
out = irradiance.perez(40, 180, irrad_data['dhi'], irrad_data['dni'],
dni_et, ephem_data['apparent_zenith'],
ephem_data['azimuth'], am)
assert not out.isnull().any()
def get_irradiance(self, dni, ghi, dhi,
dni_extra=None, airmass=None, model='haydavies',
**kwargs):
"""
Uses the :func:`irradiance.total_irrad` function to calculate
the plane of array irradiance components on a tilted surface
defined by
``self.surface_tilt``, ``self.surface_azimuth``, and
``self.albedo``.
Parameters
----------
solar_zenith : float or Series.
Solar zenith angle.
solar_azimuth : float or Series.
Solar azimuth angle.
dni : float or Series
Direct Normal Irradiance
ghi : float or Series
Global horizontal irradiance
dhi : float or Series
Diffuse horizontal irradiance
dni_extra : float or Series
Extraterrestrial direct normal irradiance
airmass : float or Series
Airmass
model : String
Irradiance model.
**kwargs
Passed to :func:`irradiance.total_irrad`.
Returns
-------
poa_irradiance : DataFrame
Column names are: ``total, beam, sky, ground``.
"""
surface_tilt = kwargs.pop('surface_tilt', self.surface_tilt)
surface_azimuth = kwargs.pop('surface_azimuth', self.surface_azimuth)
try:
solar_zenith = kwargs['solar_zenith']
except KeyError:
solar_zenith = self.solar_zenith
try:
solar_azimuth = kwargs['solar_azimuth']
except KeyError:
solar_azimuth = self.solar_azimuth
# not needed for all models, but this is easier
if dni_extra is None:
dni_extra = irradiance.extraradiation(solar_zenith.index)
dni_extra = pd.Series(dni_extra, index=solar_zenith.index)
if airmass is None:
airmass = atmosphere.relativeairmass(solar_zenith)
return irradiance.total_irrad(surface_tilt,
surface_azimuth,
solar_zenith,
solar_azimuth,
dni, ghi, dhi,
dni_extra=dni_extra, airmass=airmass,
model=model,
albedo=self.albedo,
**kwargs)
def perez_diffuse_luminance(df_inputs):
"""
Function used to calculate the luminance and the view factor terms from the
Perez diffuse light transposition model, as implemented in the
``pvlib-python`` library.
:param df_inputs: class:`pandas.DataFrame` with following columns:
['solar_zenith', 'solar_azimuth', 'array_tilt', 'array_azimuth', 'dhi',
'dni']. Units are: ['deg', 'deg', 'deg', 'deg', 'W/m2', 'W/m2']
:return: class:`pandas.DataFrame` with the following columns:
['solar_zenith', 'solar_azimuth', 'array_tilt', 'array_azimuth', 'dhi',
'dni', 'vf_horizon', 'vf_circumsolar', 'vf_isotropic',
'luminance_horizon', 'luminance_circumsolar', 'luminance_isotropic',
'poa_isotropic', 'poa_circumsolar', 'poa_horizon', 'poa_total_diffuse']
"""
dni_et = irradiance.extraradiation(df_inputs.index.dayofyear)
am = atmosphere.relativeairmass(df_inputs.solar_zenith)
# Need to treat the case when the sun is hitting the back surface of pvrow
aoi_proj = aoi_projection(df_inputs.array_tilt, df_inputs.array_azimuth,
df_inputs.solar_zenith, df_inputs.solar_azimuth)
sun_hitting_back_surface = ((aoi_proj < 0) &
(df_inputs.solar_zenith <= 90))
df_inputs_back_surface = df_inputs.loc[sun_hitting_back_surface]
# Reverse the surface normal to switch to back-surface circumsolar calc
df_inputs_back_surface.loc[:, 'array_azimuth'] -= 180.
df_inputs_back_surface.loc[:, 'array_azimuth'] = np.mod(
df_inputs_back_surface.loc[:, 'array_azimuth'], 360.
)
df_inputs_back_surface.loc[:, 'array_tilt'] = (
180. - df_inputs_back_surface.array_tilt)
if df_inputs_back_surface.shape[0] > 0:
# Use recursion to calculate circumsolar luminance for back surface
df_inputs_back_surface = perez_diffuse_luminance(
df_inputs_back_surface)
# Calculate Perez diffuse components
diffuse_poa, components = irradiance.perez(df_inputs.array_tilt,
df_inputs.array_azimuth,
df_inputs.dhi, df_inputs.dni,
dni_et,
df_inputs.solar_zenith,
df_inputs.solar_azimuth,
am,
return_components=True)
# Calculate Perez view factors:
a = aoi_projection(df_inputs.array_tilt, df_inputs.array_azimuth,
df_inputs.solar_zenith, df_inputs.solar_azimuth)
a = np.maximum(a, 0)
b = cosd(df_inputs.solar_zenith)
b = np.maximum(b, cosd(85))
vf_perez = pd.DataFrame(
np.array([
sind(df_inputs.array_tilt),
a / b,
(1. + cosd(df_inputs.array_tilt)) / 2.
]).T,
index=df_inputs.index,
columns=['vf_horizon', 'vf_circumsolar', 'vf_isotropic']
)
# Calculate diffuse luminance
luminance = pd.DataFrame(
np.array([
components['horizon'] / vf_perez['vf_horizon'],
components['circumsolar'] / vf_perez['vf_circumsolar'],
components['isotropic'] / vf_perez['vf_isotropic']
]).T,
index=df_inputs.index,
columns=['luminance_horizon', 'luminance_circumsolar',
'luminance_isotropic']
)
luminance.loc[diffuse_poa == 0, :] = 0.
# Format components column names
components = components.rename(columns={'isotropic': 'poa_isotropic',
'circumsolar': 'poa_circumsolar',
'horizon': 'poa_horizon'})
df_inputs = pd.concat([df_inputs, components, vf_perez, luminance,
diffuse_poa],
axis=1, join='outer')
df_inputs = df_inputs.rename(columns={0: 'poa_total_diffuse'})
# Adjust the circumsolar luminance when it hits the back surface
if df_inputs_back_surface.shape[0] > 0:
df_inputs.loc[sun_hitting_back_surface, 'luminance_circumsolar'] = (
df_inputs_back_surface.loc[:, 'luminance_circumsolar']
)
return df_inputs
def test_deprecated_07():
with pytest.warns(pvlibDeprecationWarning):
atmosphere.relativeairmass(2)
with pytest.warns(pvlibDeprecationWarning):
atmosphere.absoluteairmass(2)
def run_airmass(model, zenith):
atmosphere.relativeairmass(zenith, model)
def get_irradiance(self,
surface_tilt,
surface_azimuth,
solar_zenith,
solar_azimuth,
dni,
ghi,
dhi,
dni_extra=None,
airmass=None,
model='haydavies',
**kwargs):
"""
Uses the :func:`irradiance.total_irrad` function to calculate
the plane of array irradiance components on a tilted surface
defined by the input data and ``self.albedo``.
For a given set of solar zenith and azimuth angles, the
surface tilt and azimuth parameters are typically determined
by :py:method:`~SingleAxisTracker.singleaxis`.
Parameters
----------
surface_tilt : numeric
Panel tilt from horizontal.
surface_azimuth : numeric
Panel azimuth from north
solar_zenith : numeric
Solar zenith angle.
solar_azimuth : numeric
Solar azimuth angle.
dni : float or Series
Direct Normal Irradiance
ghi : float or Series
Global horizontal irradiance
dhi : float or Series
Diffuse horizontal irradiance
dni_extra : float or Series, default None
Extraterrestrial direct normal irradiance
airmass : float or Series, default None
Airmass
model : String, default 'haydavies'
Irradiance model.
**kwargs
Passed to :func:`irradiance.total_irrad`.
Returns
-------
poa_irradiance : DataFrame
Column names are: ``total, beam, sky, ground``.
"""
# not needed for all models, but this is easier
if dni_extra is None:
dni_extra = irradiance.extraradiation(solar_zenith.index)
if airmass is None:
airmass = atmosphere.relativeairmass(solar_zenith)
return irradiance.total_irrad(surface_tilt,
surface_azimuth,
solar_zenith,
solar_azimuth,
dni,
ghi,
dhi,
dni_extra=dni_extra,
airmass=airmass,
model=model,
albedo=self.albedo,
**kwargs)
def ineichen(time, latitude, longitude, altitude=0, linke_turbidity=None,
solarposition_method='nrel_numpy', zenith_data=None,
airmass_model='young1994', airmass_data=None,
interp_turbidity=True):
'''
Determine clear sky GHI, DNI, and DHI from Ineichen/Perez model
Implements the Ineichen and Perez clear sky model for global horizontal
irradiance (GHI), direct normal irradiance (DNI), and calculates
the clear-sky diffuse horizontal (DHI) component as the difference
between GHI and DNI*cos(zenith) as presented in [1, 2]. A report on clear
sky models found the Ineichen/Perez model to have excellent performance
with a minimal input data set [3].
Default values for montly Linke turbidity provided by SoDa [4, 5].
Parameters
-----------
time : pandas.DatetimeIndex
latitude : float
longitude : float
altitude : float
linke_turbidity : None or float
If None, uses ``LinkeTurbidities.mat`` lookup table.
solarposition_method : string
Sets the solar position algorithm.
See solarposition.get_solarposition()
zenith_data : None or Series
If None, ephemeris data will be calculated using ``solarposition_method``.
airmass_model : string
See pvlib.airmass.relativeairmass().
airmass_data : None or Series
If None, absolute air mass data will be calculated using
``airmass_model`` and location.alitude.
interp_turbidity : bool
If ``True``, interpolates the monthly Linke turbidity values
found in ``LinkeTurbidities.mat`` to daily values.
Returns
--------
DataFrame with the following columns: ``ghi, dni, dhi``.
Notes
-----
If you are using this function
in a loop, it may be faster to load LinkeTurbidities.mat outside of
the loop and feed it in as a keyword argument, rather than
having the function open and process the file each time it is called.
References
----------
[1] P. Ineichen and R. Perez, "A New airmass independent formulation for
the Linke turbidity coefficient", Solar Energy, vol 73, pp. 151-157, 2002.
[2] R. Perez et. al., "A New Operational Model for Satellite-Derived
Irradiances: Description and Validation", Solar Energy, vol 73, pp.
307-317, 2002.
[3] M. Reno, C. Hansen, and J. Stein, "Global Horizontal Irradiance Clear
Sky Models: Implementation and Analysis", Sandia National
Laboratories, SAND2012-2389, 2012.
[4] http://www.soda-is.com/eng/services/climat_free_eng.php#c5 (obtained
July 17, 2012).
[5] J. Remund, et. al., "Worldwide Linke Turbidity Information", Proc.
ISES Solar World Congress, June 2003. Goteborg, Sweden.
'''
# Initial implementation of this algorithm by Matthew Reno.
# Ported to python by Rob Andrews
# Added functionality by Will Holmgren (@wholmgren)
I0 = irradiance.extraradiation(time.dayofyear)
if zenith_data is None:
ephem_data = solarposition.get_solarposition(time,
latitude=latitude,
longitude=longitude,
altitude=altitude,
method=solarposition_method)
time = ephem_data.index # fixes issue with time possibly not being tz-aware
try:
ApparentZenith = ephem_data['apparent_zenith']
except KeyError:
ApparentZenith = ephem_data['zenith']
logger.warning('could not find apparent_zenith. using zenith')
else:
ApparentZenith = zenith_data
#ApparentZenith[ApparentZenith >= 90] = 90 # can cause problems in edge cases
if linke_turbidity is None:
TL = lookup_linke_turbidity(time, latitude, longitude,
interp_turbidity=interp_turbidity)
else:
TL = linke_turbidity
# Get the absolute airmass assuming standard local pressure (per
# alt2pres) using Kasten and Young's 1989 formula for airmass.
if airmass_data is None:
AMabsolute = atmosphere.absoluteairmass(airmass_relative=atmosphere.relativeairmass(ApparentZenith, airmass_model),
pressure=atmosphere.alt2pres(altitude))
else:
AMabsolute = airmass_data
fh1 = np.exp(-altitude/8000.)
fh2 = np.exp(-altitude/1250.)
cg1 = 5.09e-05 * altitude + 0.868
cg2 = 3.92e-05 * altitude + 0.0387
logger.debug('fh1=%s, fh2=%s, cg1=%s, cg2=%s', fh1, fh2, cg1, cg2)
# Dan's note on the TL correction: By my reading of the publication on
# pages 151-157, Ineichen and Perez introduce (among other things) three
# things. 1) Beam model in eqn. 8, 2) new turbidity factor in eqn 9 and
# appendix A, and 3) Global horizontal model in eqn. 11. They do NOT appear
# to use the new turbidity factor (item 2 above) in either the beam or GHI
# models. The phrasing of appendix A seems as if there are two separate
# corrections, the first correction is used to correct the beam/GHI models,
# and the second correction is used to correct the revised turibidity
# factor. In my estimation, there is no need to correct the turbidity
# factor used in the beam/GHI models.
# Create the corrected TL for TL < 2
# TLcorr = TL;
# TLcorr(TL < 2) = TLcorr(TL < 2) - 0.25 .* (2-TLcorr(TL < 2)) .^ (0.5);
# This equation is found in Solar Energy 73, pg 311.
# Full ref: Perez et. al., Vol. 73, pp. 307-317 (2002).
# It is slightly different than the equation given in Solar Energy 73, pg 156.
# We used the equation from pg 311 because of the existence of known typos
# in the pg 156 publication (notably the fh2-(TL-1) should be fh2 * (TL-1)).
cos_zenith = tools.cosd(ApparentZenith)
clearsky_GHI = ( cg1 * I0 * cos_zenith *
np.exp(-cg2*AMabsolute*(fh1 + fh2*(TL - 1))) *
np.exp(0.01*AMabsolute**1.8) )
clearsky_GHI[clearsky_GHI < 0] = 0
# BncI == "normal beam clear sky radiation"
b = 0.664 + 0.163/fh1
BncI = b * I0 * np.exp( -0.09 * AMabsolute * (TL - 1) )
logger.debug('b=%s', b)
# "empirical correction" SE 73, 157 & SE 73, 312.
BncI_2 = ( clearsky_GHI *
( 1 - (0.1 - 0.2*np.exp(-TL))/(0.1 + 0.882/fh1) ) /
cos_zenith )
clearsky_DNI = np.minimum(BncI, BncI_2)
clearsky_DHI = clearsky_GHI - clearsky_DNI*cos_zenith
df_out = pd.DataFrame({'ghi':clearsky_GHI, 'dni':clearsky_DNI,
'dhi':clearsky_DHI})
df_out.fillna(0, inplace=True)
return df_out
def ineichen(time,
location,
linke_turbidity=None,
solarposition_method='pyephem',
zenith_data=None,
airmass_model='young1994',
airmass_data=None,
interp_turbidity=True):
'''
Determine clear sky GHI, DNI, and DHI from Ineichen/Perez model
Implements the Ineichen and Perez clear sky model for global horizontal
irradiance (GHI), direct normal irradiance (DNI), and calculates
the clear-sky diffuse horizontal (DHI) component as the difference
between GHI and DNI*cos(zenith) as presented in [1, 2]. A report on clear
sky models found the Ineichen/Perez model to have excellent performance
with a minimal input data set [3].
Default values for montly Linke turbidity provided by SoDa [4, 5].
Parameters
-----------
time : pandas.DatetimeIndex
location : pvlib.Location
linke_turbidity : None or float
If None, uses ``LinkeTurbidities.mat`` lookup table.
solarposition_method : string
Sets the solar position algorithm.
See solarposition.get_solarposition()
zenith_data : None or Series
If None, ephemeris data will be calculated using ``solarposition_method``.
airmass_model : string
See pvlib.airmass.relativeairmass().
airmass_data : None or Series
If None, absolute air mass data will be calculated using
``airmass_model`` and location.alitude.
interp_turbidity : bool
If ``True``, interpolates the monthly Linke turbidity values
found in ``LinkeTurbidities.mat`` to daily values.
Returns
--------
DataFrame with the following columns: ``ghi, dni, dhi``.
Notes
-----
If you are using this function
in a loop, it may be faster to load LinkeTurbidities.mat outside of
the loop and feed it in as a keyword argument, rather than
having the function open and process the file each time it is called.
References
----------
[1] P. Ineichen and R. Perez, "A New airmass independent formulation for
the Linke turbidity coefficient", Solar Energy, vol 73, pp. 151-157, 2002.
[2] R. Perez et. al., "A New Operational Model for Satellite-Derived
Irradiances: Description and Validation", Solar Energy, vol 73, pp.
307-317, 2002.
[3] M. Reno, C. Hansen, and J. Stein, "Global Horizontal Irradiance Clear
Sky Models: Implementation and Analysis", Sandia National
Laboratories, SAND2012-2389, 2012.
[4] http://www.soda-is.com/eng/services/climat_free_eng.php#c5 (obtained
July 17, 2012).
[5] J. Remund, et. al., "Worldwide Linke Turbidity Information", Proc.
ISES Solar World Congress, June 2003. Goteborg, Sweden.
'''
# Initial implementation of this algorithm by Matthew Reno.
# Ported to python by Rob Andrews
# Added functionality by Will Holmgren (@wholmgren)
I0 = irradiance.extraradiation(time.dayofyear)
if zenith_data is None:
ephem_data = solarposition.get_solarposition(
time, location, method=solarposition_method)
time = ephem_data.index # fixes issue with time possibly not being tz-aware
try:
ApparentZenith = ephem_data['apparent_zenith']
except KeyError:
ApparentZenith = ephem_data['zenith']
logger.warning('could not find apparent_zenith. using zenith')
else:
ApparentZenith = zenith_data
#ApparentZenith[ApparentZenith >= 90] = 90 # can cause problems in edge cases
if linke_turbidity is None:
TL = lookup_linke_turbidity(time,
location.latitude,
location.longitude,
interp_turbidity=interp_turbidity)
else:
TL = linke_turbidity
# Get the absolute airmass assuming standard local pressure (per
# alt2pres) using Kasten and Young's 1989 formula for airmass.
if airmass_data is None:
AMabsolute = atmosphere.absoluteairmass(
airmass_relative=atmosphere.relativeairmass(
ApparentZenith, airmass_model),
pressure=atmosphere.alt2pres(location.altitude))
else:
AMabsolute = airmass_data
fh1 = np.exp(-location.altitude / 8000.)
fh2 = np.exp(-location.altitude / 1250.)
cg1 = 5.09e-05 * location.altitude + 0.868
cg2 = 3.92e-05 * location.altitude + 0.0387
logger.debug('fh1=%s, fh2=%s, cg1=%s, cg2=%s', fh1, fh2, cg1, cg2)
# Dan's note on the TL correction: By my reading of the publication on
# pages 151-157, Ineichen and Perez introduce (among other things) three
# things. 1) Beam model in eqn. 8, 2) new turbidity factor in eqn 9 and
# appendix A, and 3) Global horizontal model in eqn. 11. They do NOT appear
# to use the new turbidity factor (item 2 above) in either the beam or GHI
# models. The phrasing of appendix A seems as if there are two separate
# corrections, the first correction is used to correct the beam/GHI models,
# and the second correction is used to correct the revised turibidity
# factor. In my estimation, there is no need to correct the turbidity
# factor used in the beam/GHI models.
# Create the corrected TL for TL < 2
# TLcorr = TL;
# TLcorr(TL < 2) = TLcorr(TL < 2) - 0.25 .* (2-TLcorr(TL < 2)) .^ (0.5);
# This equation is found in Solar Energy 73, pg 311.
# Full ref: Perez et. al., Vol. 73, pp. 307-317 (2002).
# It is slightly different than the equation given in Solar Energy 73, pg 156.
# We used the equation from pg 311 because of the existence of known typos
# in the pg 156 publication (notably the fh2-(TL-1) should be fh2 * (TL-1)).
cos_zenith = tools.cosd(ApparentZenith)
clearsky_GHI = (cg1 * I0 * cos_zenith * np.exp(-cg2 * AMabsolute *
(fh1 + fh2 * (TL - 1))) *
np.exp(0.01 * AMabsolute**1.8))
clearsky_GHI[clearsky_GHI < 0] = 0
# BncI == "normal beam clear sky radiation"
b = 0.664 + 0.163 / fh1
BncI = b * I0 * np.exp(-0.09 * AMabsolute * (TL - 1))
logger.debug('b=%s', b)
# "empirical correction" SE 73, 157 & SE 73, 312.
BncI_2 = (clearsky_GHI * (1 - (0.1 - 0.2 * np.exp(-TL)) /
(0.1 + 0.882 / fh1)) / cos_zenith)
clearsky_DNI = np.minimum(BncI, BncI_2)
clearsky_DHI = clearsky_GHI - clearsky_DNI * cos_zenith
df_out = pd.DataFrame({
'ghi': clearsky_GHI,
'dni': clearsky_DNI,
'dhi': clearsky_DHI
})
df_out.fillna(0, inplace=True)
return df_out
def basic_chain(times,
latitude,
longitude,
module_parameters,
inverter_parameters,
irradiance=None,
weather=None,
surface_tilt=None,
surface_azimuth=None,
orientation_strategy=None,
transposition_model='haydavies',
solar_position_method='nrel_numpy',
airmass_model='kastenyoung1989',
altitude=None,
pressure=None,
**kwargs):
"""
An experimental function that computes all of the modeling steps
necessary for calculating power or energy for a PV system at a given
location.
Parameters
----------
times : DatetimeIndex
Times at which to evaluate the model.
latitude : float.
Positive is north of the equator.
Use decimal degrees notation.
longitude : float.
Positive is east of the prime meridian.
Use decimal degrees notation.
module_parameters : None, dict or Series
Module parameters as defined by the SAPM.
inverter_parameters : None, dict or Series
Inverter parameters as defined by the CEC.
irradiance : None or DataFrame
If None, calculates clear sky data.
Columns must be 'dni', 'ghi', 'dhi'.
weather : None or DataFrame
If None, assumes air temperature is 20 C and
wind speed is 0 m/s.
Columns must be 'wind_speed', 'temp_air'.
surface_tilt : float or Series
Surface tilt angles in decimal degrees.
The tilt angle is defined as degrees from horizontal
(e.g. surface facing up = 0, surface facing horizon = 90)
surface_azimuth : float or Series
Surface azimuth angles in decimal degrees.
The azimuth convention is defined
as degrees east of north
(North=0, South=180, East=90, West=270).
orientation_strategy : None or str
The strategy for aligning the modules.
If not None, sets the ``surface_azimuth`` and ``surface_tilt``
properties of the ``system``. Allowed strategies include 'flat',
'south_at_latitude_tilt'. Ignored for SingleAxisTracker systems.
transposition_model : str
Passed to system.get_irradiance.
solar_position_method : str
Passed to location.get_solarposition.
airmass_model : str
Passed to location.get_airmass.
altitude : None or float
If None, computed from pressure. Assumed to be 0 m
if pressure is also None.
pressure : None or float
If None, computed from altitude. Assumed to be 101325 Pa
if altitude is also None.
**kwargs
Arbitrary keyword arguments.
See code for details.
Returns
-------
output : (dc, ac)
Tuple of DC power (with SAPM parameters) (DataFrame) and AC
power (Series).
"""
# use surface_tilt and surface_azimuth if provided,
# otherwise set them using the orientation_strategy
if surface_tilt is not None and surface_azimuth is not None:
pass
elif orientation_strategy is not None:
surface_tilt, surface_azimuth = \
get_orientation(orientation_strategy, latitude=latitude)
else:
raise ValueError('orientation_strategy or surface_tilt and ' +
'surface_azimuth must be provided')
times = times
if altitude is None and pressure is None:
altitude = 0.
pressure = 101325.
elif altitude is None:
altitude = atmosphere.pres2alt(pressure)
elif pressure is None:
pressure = atmosphere.alt2pres(altitude)
solar_position = solarposition.get_solarposition(times,
latitude,
longitude,
altitude=altitude,
pressure=pressure,
**kwargs)
# possible error with using apparent zenith with some models
airmass = atmosphere.relativeairmass(solar_position['apparent_zenith'],
model=airmass_model)
airmass = atmosphere.absoluteairmass(airmass, pressure)
dni_extra = pvlib.irradiance.extraradiation(solar_position.index)
dni_extra = pd.Series(dni_extra, index=solar_position.index)
aoi = pvlib.irradiance.aoi(surface_tilt, surface_azimuth,
solar_position['apparent_zenith'],
solar_position['azimuth'])
if irradiance is None:
linke_turbidity = clearsky.lookup_linke_turbidity(
solar_position.index, latitude, longitude)
irradiance = clearsky.ineichen(solar_position['apparent_zenith'],
airmass,
linke_turbidity,
altitude=altitude,
dni_extra=dni_extra)
total_irrad = pvlib.irradiance.total_irrad(
surface_tilt,
surface_azimuth,
solar_position['apparent_zenith'],
solar_position['azimuth'],
irradiance['dni'],
irradiance['ghi'],
irradiance['dhi'],
model=transposition_model,
dni_extra=dni_extra)
if weather is None:
weather = {'wind_speed': 0, 'temp_air': 20}
temps = pvsystem.sapm_celltemp(total_irrad['poa_global'],
weather['wind_speed'], weather['temp_air'])
effective_irradiance = pvsystem.sapm_effective_irradiance(
total_irrad['poa_direct'], total_irrad['poa_diffuse'], airmass, aoi,
module_parameters)
dc = pvsystem.sapm(effective_irradiance, temps['temp_cell'],
module_parameters)
ac = pvsystem.snlinverter(dc['v_mp'], dc['p_mp'], inverter_parameters)
return dc, ac
def test_airmass_scalar_nan():
assert np.isnan(atmosphere.relativeairmass(100))
def ineichen(
time,
location,
linke_turbidity=None,
solarposition_method="pyephem",
zenith_data=None,
airmass_model="young1994",
airmass_data=None,
interp_turbidity=True,
):
"""
Determine clear sky GHI, DNI, and DHI from Ineichen/Perez model
Implements the Ineichen and Perez clear sky model for global horizontal
irradiance (GHI), direct normal irradiance (DNI), and calculates
the clear-sky diffuse horizontal (DHI) component as the difference
between GHI and DNI*cos(zenith) as presented in [1, 2]. A report on clear
sky models found the Ineichen/Perez model to have excellent performance
with a minimal input data set [3].
Default values for montly Linke turbidity provided by SoDa [4, 5].
Parameters
-----------
time : pandas.DatetimeIndex
location : pvlib.Location
linke_turbidity : None or float
If None, uses ``LinkeTurbidities.mat`` lookup table.
solarposition_method : string
Sets the solar position algorithm.
See solarposition.get_solarposition()
zenith_data : None or pandas.Series
If None, ephemeris data will be calculated using ``solarposition_method``.
airmass_model : string
See pvlib.airmass.relativeairmass().
airmass_data : None or pandas.Series
If None, absolute air mass data will be calculated using
``airmass_model`` and location.alitude.
interp_turbidity : bool
If ``True``, interpolates the monthly Linke turbidity values
found in ``LinkeTurbidities.mat`` to daily values.
Returns
--------
DataFrame with the following columns: ``GHI, DNI, DHI``.
Notes
-----
If you are using this function
in a loop, it may be faster to load LinkeTurbidities.mat outside of
the loop and feed it in as a variable, rather than
having the function open the file each time it is called.
References
----------
[1] P. Ineichen and R. Perez, "A New airmass independent formulation for
the Linke turbidity coefficient", Solar Energy, vol 73, pp. 151-157, 2002.
[2] R. Perez et. al., "A New Operational Model for Satellite-Derived
Irradiances: Description and Validation", Solar Energy, vol 73, pp.
307-317, 2002.
[3] M. Reno, C. Hansen, and J. Stein, "Global Horizontal Irradiance Clear
Sky Models: Implementation and Analysis", Sandia National
Laboratories, SAND2012-2389, 2012.
[4] http://www.soda-is.com/eng/services/climat_free_eng.php#c5 (obtained
July 17, 2012).
[5] J. Remund, et. al., "Worldwide Linke Turbidity Information", Proc.
ISES Solar World Congress, June 2003. Goteborg, Sweden.
"""
# Initial implementation of this algorithm by Matthew Reno.
# Ported to python by Rob Andrews
# Added functionality by Will Holmgren
I0 = irradiance.extraradiation(time.dayofyear)
if zenith_data is None:
ephem_data = solarposition.get_solarposition(time, location, method=solarposition_method)
time = ephem_data.index # fixes issue with time possibly not being tz-aware
try:
ApparentZenith = ephem_data["apparent_zenith"]
except KeyError:
ApparentZenith = ephem_data["zenith"]
logger.warning("could not find apparent_zenith. using zenith")
else:
ApparentZenith = zenith_data
# ApparentZenith[ApparentZenith >= 90] = 90 # can cause problems in edge cases
if linke_turbidity is None:
# The .mat file 'LinkeTurbidities.mat' contains a single 2160 x 4320 x 12
# matrix of type uint8 called 'LinkeTurbidity'. The rows represent global
# latitudes from 90 to -90 degrees; the columns represent global longitudes
# from -180 to 180; and the depth (third dimension) represents months of
# the year from January (1) to December (12). To determine the Linke
# turbidity for a position on the Earth's surface for a given month do the
# following: LT = LinkeTurbidity(LatitudeIndex, LongitudeIndex, month).
# Note that the numbers within the matrix are 20 * Linke Turbidity,
# so divide the number from the file by 20 to get the
# turbidity.
try:
import scipy.io
except ImportError:
raise ImportError(
"The Linke turbidity lookup table requires scipy. "
+ "You can still use clearsky.ineichen if you "
+ "supply your own turbidities."
)
# consider putting this code at module level
this_path = os.path.dirname(os.path.abspath(__file__))
logger.debug("this_path={}".format(this_path))
mat = scipy.io.loadmat(os.path.join(this_path, "data", "LinkeTurbidities.mat"))
linke_turbidity = mat["LinkeTurbidity"]
LatitudeIndex = np.round_(_linearly_scale(location.latitude, 90, -90, 1, 2160))
LongitudeIndex = np.round_(_linearly_scale(location.longitude, -180, 180, 1, 4320))
g = linke_turbidity[LatitudeIndex][LongitudeIndex]
if interp_turbidity:
logger.info("interpolating turbidity to the day")
g2 = np.concatenate([[g[-1]], g, [g[0]]]) # wrap ends around
days = np.linspace(-15, 380, num=14) # map day of year onto month (approximate)
LT = pd.Series(np.interp(time.dayofyear, days, g2), index=time)
else:
logger.info("using monthly turbidity")
ApplyMonth = lambda x: g[x[0] - 1]
LT = pd.DataFrame(time.month, index=time)
LT = LT.apply(ApplyMonth, axis=1)
TL = LT / 20.0
logger.info("using TL=\n{}".format(TL))
else:
TL = linke_turbidity
# Get the absolute airmass assuming standard local pressure (per
# alt2pres) using Kasten and Young's 1989 formula for airmass.
if airmass_data is None:
AMabsolute = atmosphere.absoluteairmass(
AMrelative=atmosphere.relativeairmass(ApparentZenith, airmass_model),
pressure=atmosphere.alt2pres(location.altitude),
)
else:
AMabsolute = airmass_data
fh1 = np.exp(-location.altitude / 8000.0)
fh2 = np.exp(-location.altitude / 1250.0)
cg1 = 5.09e-05 * location.altitude + 0.868
cg2 = 3.92e-05 * location.altitude + 0.0387
logger.debug("fh1={}, fh2={}, cg1={}, cg2={}".format(fh1, fh2, cg1, cg2))
# Dan's note on the TL correction: By my reading of the publication on
# pages 151-157, Ineichen and Perez introduce (among other things) three
# things. 1) Beam model in eqn. 8, 2) new turbidity factor in eqn 9 and
# appendix A, and 3) Global horizontal model in eqn. 11. They do NOT appear
# to use the new turbidity factor (item 2 above) in either the beam or GHI
# models. The phrasing of appendix A seems as if there are two separate
# corrections, the first correction is used to correct the beam/GHI models,
# and the second correction is used to correct the revised turibidity
# factor. In my estimation, there is no need to correct the turbidity
# factor used in the beam/GHI models.
# Create the corrected TL for TL < 2
# TLcorr = TL;
# TLcorr(TL < 2) = TLcorr(TL < 2) - 0.25 .* (2-TLcorr(TL < 2)) .^ (0.5);
# This equation is found in Solar Energy 73, pg 311.
# Full ref: Perez et. al., Vol. 73, pp. 307-317 (2002).
# It is slightly different than the equation given in Solar Energy 73, pg 156.
# We used the equation from pg 311 because of the existence of known typos
# in the pg 156 publication (notably the fh2-(TL-1) should be fh2 * (TL-1)).
cos_zenith = tools.cosd(ApparentZenith)
clearsky_GHI = (
cg1 * I0 * cos_zenith * np.exp(-cg2 * AMabsolute * (fh1 + fh2 * (TL - 1))) * np.exp(0.01 * AMabsolute ** 1.8)
)
clearsky_GHI[clearsky_GHI < 0] = 0
# BncI == "normal beam clear sky radiation"
b = 0.664 + 0.163 / fh1
BncI = b * I0 * np.exp(-0.09 * AMabsolute * (TL - 1))
logger.debug("b={}".format(b))
# "empirical correction" SE 73, 157 & SE 73, 312.
BncI_2 = clearsky_GHI * (1 - (0.1 - 0.2 * np.exp(-TL)) / (0.1 + 0.882 / fh1)) / cos_zenith
# return BncI, BncI_2
clearsky_DNI = np.minimum(BncI, BncI_2) # Will H: use np.minimum explicitly
clearsky_DHI = clearsky_GHI - clearsky_DNI * cos_zenith
df_out = pd.DataFrame({"GHI": clearsky_GHI, "DNI": clearsky_DNI, "DHI": clearsky_DHI})
df_out.fillna(0, inplace=True)
# df_out['BncI'] = BncI
# df_out['BncI_2'] = BncI
return df_out
def test_absoluteairmass():
relative_am = atmosphere.relativeairmass(ephem_data['zenith'], 'simple')
atmosphere.absoluteairmass(relative_am)
atmosphere.absoluteairmass(relative_am, pressure=100000)
def ineichen(time,
location,
linke_turbidity=None,
solarposition_method='pyephem',
zenith_data=None,
airmass_model='young1994',
airmass_data=None,
interp_turbidity=True):
'''
Determine clear sky GHI, DNI, and DHI from Ineichen/Perez model
Implements the Ineichen and Perez clear sky model for global horizontal
irradiance (GHI), direct normal irradiance (DNI), and calculates
the clear-sky diffuse horizontal (DHI) component as the difference
between GHI and DNI*cos(zenith) as presented in [1, 2]. A report on clear
sky models found the Ineichen/Perez model to have excellent performance
with a minimal input data set [3].
Default values for montly Linke turbidity provided by SoDa [4, 5].
Parameters
-----------
time : pandas.DatetimeIndex
location : pvlib.Location
linke_turbidity : None or float
If None, uses ``LinkeTurbidities.mat`` lookup table.
solarposition_method : string
Sets the solar position algorithm.
See solarposition.get_solarposition()
zenith_data : None or pandas.Series
If None, ephemeris data will be calculated using ``solarposition_method``.
airmass_model : string
See pvlib.airmass.relativeairmass().
airmass_data : None or pandas.Series
If None, absolute air mass data will be calculated using
``airmass_model`` and location.alitude.
interp_turbidity : bool
If ``True``, interpolates the monthly Linke turbidity values
found in ``LinkeTurbidities.mat`` to daily values.
Returns
--------
DataFrame with the following columns: ``GHI, DNI, DHI``.
Notes
-----
If you are using this function
in a loop, it may be faster to load LinkeTurbidities.mat outside of
the loop and feed it in as a variable, rather than
having the function open the file each time it is called.
References
----------
[1] P. Ineichen and R. Perez, "A New airmass independent formulation for
the Linke turbidity coefficient", Solar Energy, vol 73, pp. 151-157, 2002.
[2] R. Perez et. al., "A New Operational Model for Satellite-Derived
Irradiances: Description and Validation", Solar Energy, vol 73, pp.
307-317, 2002.
[3] M. Reno, C. Hansen, and J. Stein, "Global Horizontal Irradiance Clear
Sky Models: Implementation and Analysis", Sandia National
Laboratories, SAND2012-2389, 2012.
[4] http://www.soda-is.com/eng/services/climat_free_eng.php#c5 (obtained
July 17, 2012).
[5] J. Remund, et. al., "Worldwide Linke Turbidity Information", Proc.
ISES Solar World Congress, June 2003. Goteborg, Sweden.
'''
# Initial implementation of this algorithm by Matthew Reno.
# Ported to python by Rob Andrews
# Added functionality by Will Holmgren
I0 = irradiance.extraradiation(time.dayofyear)
if zenith_data is None:
ephem_data = solarposition.get_solarposition(
time, location, method=solarposition_method)
time = ephem_data.index # fixes issue with time possibly not being tz-aware
try:
ApparentZenith = ephem_data['apparent_zenith']
except KeyError:
ApparentZenith = ephem_data['zenith']
logger.warning('could not find apparent_zenith. using zenith')
else:
ApparentZenith = zenith_data
#ApparentZenith[ApparentZenith >= 90] = 90 # can cause problems in edge cases
if linke_turbidity is None:
# The .mat file 'LinkeTurbidities.mat' contains a single 2160 x 4320 x 12
# matrix of type uint8 called 'LinkeTurbidity'. The rows represent global
# latitudes from 90 to -90 degrees; the columns represent global longitudes
# from -180 to 180; and the depth (third dimension) represents months of
# the year from January (1) to December (12). To determine the Linke
# turbidity for a position on the Earth's surface for a given month do the
# following: LT = LinkeTurbidity(LatitudeIndex, LongitudeIndex, month).
# Note that the numbers within the matrix are 20 * Linke Turbidity,
# so divide the number from the file by 20 to get the
# turbidity.
try:
import scipy.io
except ImportError:
raise ImportError(
'The Linke turbidity lookup table requires scipy. ' +
'You can still use clearsky.ineichen if you ' +
'supply your own turbidities.')
# consider putting this code at module level
this_path = os.path.dirname(os.path.abspath(__file__))
logger.debug('this_path={}'.format(this_path))
mat = scipy.io.loadmat(
os.path.join(this_path, 'data', 'LinkeTurbidities.mat'))
linke_turbidity = mat['LinkeTurbidity']
LatitudeIndex = np.round_(
_linearly_scale(location.latitude, 90, -90, 1, 2160))
LongitudeIndex = np.round_(
_linearly_scale(location.longitude, -180, 180, 1, 4320))
g = linke_turbidity[LatitudeIndex][LongitudeIndex]
if interp_turbidity:
logger.info('interpolating turbidity to the day')
g2 = np.concatenate([[g[-1]], g, [g[0]]]) # wrap ends around
days = np.linspace(
-15, 380, num=14) # map day of year onto month (approximate)
LT = pd.Series(np.interp(time.dayofyear, days, g2), index=time)
else:
logger.info('using monthly turbidity')
ApplyMonth = lambda x: g[x[0] - 1]
LT = pd.DataFrame(time.month, index=time)
LT = LT.apply(ApplyMonth, axis=1)
TL = LT / 20.
logger.info('using TL=\n{}'.format(TL))
else:
TL = linke_turbidity
# Get the absolute airmass assuming standard local pressure (per
# alt2pres) using Kasten and Young's 1989 formula for airmass.
if airmass_data is None:
AMabsolute = atmosphere.absoluteairmass(
AMrelative=atmosphere.relativeairmass(ApparentZenith,
airmass_model),
pressure=atmosphere.alt2pres(location.altitude))
else:
AMabsolute = airmass_data
fh1 = np.exp(-location.altitude / 8000.)
fh2 = np.exp(-location.altitude / 1250.)
cg1 = 5.09e-05 * location.altitude + 0.868
cg2 = 3.92e-05 * location.altitude + 0.0387
logger.debug('fh1={}, fh2={}, cg1={}, cg2={}'.format(fh1, fh2, cg1, cg2))
# Dan's note on the TL correction: By my reading of the publication on
# pages 151-157, Ineichen and Perez introduce (among other things) three
# things. 1) Beam model in eqn. 8, 2) new turbidity factor in eqn 9 and
# appendix A, and 3) Global horizontal model in eqn. 11. They do NOT appear
# to use the new turbidity factor (item 2 above) in either the beam or GHI
# models. The phrasing of appendix A seems as if there are two separate
# corrections, the first correction is used to correct the beam/GHI models,
# and the second correction is used to correct the revised turibidity
# factor. In my estimation, there is no need to correct the turbidity
# factor used in the beam/GHI models.
# Create the corrected TL for TL < 2
# TLcorr = TL;
# TLcorr(TL < 2) = TLcorr(TL < 2) - 0.25 .* (2-TLcorr(TL < 2)) .^ (0.5);
# This equation is found in Solar Energy 73, pg 311.
# Full ref: Perez et. al., Vol. 73, pp. 307-317 (2002).
# It is slightly different than the equation given in Solar Energy 73, pg 156.
# We used the equation from pg 311 because of the existence of known typos
# in the pg 156 publication (notably the fh2-(TL-1) should be fh2 * (TL-1)).
cos_zenith = tools.cosd(ApparentZenith)
clearsky_GHI = cg1 * I0 * cos_zenith * np.exp(
-cg2 * AMabsolute * (fh1 + fh2 *
(TL - 1))) * np.exp(0.01 * AMabsolute**1.8)
clearsky_GHI[clearsky_GHI < 0] = 0
# BncI == "normal beam clear sky radiation"
b = 0.664 + 0.163 / fh1
BncI = b * I0 * np.exp(-0.09 * AMabsolute * (TL - 1))
logger.debug('b={}'.format(b))
# "empirical correction" SE 73, 157 & SE 73, 312.
BncI_2 = clearsky_GHI * (1 - (0.1 - 0.2 * np.exp(-TL)) /
(0.1 + 0.882 / fh1)) / cos_zenith
#return BncI, BncI_2
clearsky_DNI = np.minimum(BncI,
BncI_2) # Will H: use np.minimum explicitly
clearsky_DHI = clearsky_GHI - clearsky_DNI * cos_zenith
df_out = pd.DataFrame({
'GHI': clearsky_GHI,
'DNI': clearsky_DNI,
'DHI': clearsky_DHI
})
df_out.fillna(0, inplace=True)
#df_out['BncI'] = BncI
#df_out['BncI_2'] = BncI
return df_out
def run_airmass(model, zenith):
atmosphere.relativeairmass(zenith, model)
def test_airmass_scalar_nan():
assert np.isnan(atmosphere.relativeairmass(100))
def test_absoluteairmass():
relative_am = atmosphere.relativeairmass(ephem_data['zenith'], 'simple')
atmosphere.absoluteairmass(relative_am)
atmosphere.absoluteairmass(relative_am, pressure=100000)
def get_irradiance(self, surface_tilt, surface_azimuth,
solar_zenith, solar_azimuth, dni, ghi, dhi,
dni_extra=None, airmass=None, model='haydavies',
**kwargs):
"""
Uses the :func:`irradiance.total_irrad` function to calculate
the plane of array irradiance components on a tilted surface
defined by the input data and ``self.albedo``.
For a given set of solar zenith and azimuth angles, the
surface tilt and azimuth parameters are typically determined
by :py:method:`~SingleAxisTracker.singleaxis`.
Parameters
----------
surface_tilt : numeric
Panel tilt from horizontal.
surface_azimuth : numeric
Panel azimuth from north
solar_zenith : numeric
Solar zenith angle.
solar_azimuth : numeric
Solar azimuth angle.
dni : float or Series
Direct Normal Irradiance
ghi : float or Series
Global horizontal irradiance
dhi : float or Series
Diffuse horizontal irradiance
dni_extra : float or Series, default None
Extraterrestrial direct normal irradiance
airmass : float or Series, default None
Airmass
model : String, default 'haydavies'
Irradiance model.
**kwargs
Passed to :func:`irradiance.total_irrad`.
Returns
-------
poa_irradiance : DataFrame
Column names are: ``total, beam, sky, ground``.
"""
# not needed for all models, but this is easier
if dni_extra is None:
dni_extra = irradiance.extraradiation(solar_zenith.index)
if airmass is None:
airmass = atmosphere.relativeairmass(solar_zenith)
return irradiance.total_irrad(surface_tilt,
surface_azimuth,
solar_zenith,
solar_azimuth,
dni, ghi, dhi,
dni_extra=dni_extra, airmass=airmass,
model=model,
albedo=self.albedo,
**kwargs)
def test_bird():
"""Test Bird/Hulstrom Clearsky Model"""
times = pd.DatetimeIndex(start='1/1/2015 0:00', end='12/31/2015 23:00',
freq='H')
tz = -7 # test timezone
gmt_tz = pytz.timezone('Etc/GMT%+d' % -(tz))
times = times.tz_localize(gmt_tz) # set timezone
# match test data from BIRD_08_16_2012.xls
latitude = 40.
longitude = -105.
press_mB = 840.
o3_cm = 0.3
h2o_cm = 1.5
aod_500nm = 0.1
aod_380nm = 0.15
b_a = 0.85
alb = 0.2
eot = solarposition.equation_of_time_spencer71(times.dayofyear)
hour_angle = solarposition.hour_angle(times, longitude, eot) - 0.5 * 15.
declination = solarposition.declination_spencer71(times.dayofyear)
zenith = solarposition.solar_zenith_analytical(
np.deg2rad(latitude), np.deg2rad(hour_angle), declination
)
zenith = np.rad2deg(zenith)
airmass = atmosphere.relativeairmass(zenith, model='kasten1966')
etr = irradiance.extraradiation(times)
# test Bird with time series data
field_names = ('dni', 'direct_horizontal', 'ghi', 'dhi')
irrads = clearsky.bird(
zenith, airmass, aod_380nm, aod_500nm, h2o_cm, o3_cm, press_mB * 100.,
etr, b_a, alb
)
Eb, Ebh, Gh, Dh = (irrads[_] for _ in field_names)
clearsky_path = os.path.dirname(os.path.abspath(__file__))
pvlib_path = os.path.dirname(clearsky_path)
data_path = os.path.join(pvlib_path, 'data', 'BIRD_08_16_2012.csv')
testdata = pd.read_csv(data_path, usecols=range(1, 26), header=1).dropna()
testdata.index = times[1:48]
assert np.allclose(testdata['DEC'], np.rad2deg(declination[1:48]))
assert np.allclose(testdata['EQT'], eot[1:48], rtol=1e-4)
assert np.allclose(testdata['Hour Angle'], hour_angle[1:48])
assert np.allclose(testdata['Zenith Ang'], zenith[1:48])
dawn = zenith < 88.
dusk = testdata['Zenith Ang'] < 88.
am = pd.Series(np.where(dawn, airmass, 0.), index=times).fillna(0.0)
assert np.allclose(
testdata['Air Mass'].where(dusk, 0.), am[1:48], rtol=1e-3
)
direct_beam = pd.Series(np.where(dawn, Eb, 0.), index=times).fillna(0.)
assert np.allclose(
testdata['Direct Beam'].where(dusk, 0.), direct_beam[1:48], rtol=1e-3
)
direct_horz = pd.Series(np.where(dawn, Ebh, 0.), index=times).fillna(0.)
assert np.allclose(
testdata['Direct Hz'].where(dusk, 0.), direct_horz[1:48], rtol=1e-3
)
global_horz = pd.Series(np.where(dawn, Gh, 0.), index=times).fillna(0.)
assert np.allclose(
testdata['Global Hz'].where(dusk, 0.), global_horz[1:48], rtol=1e-3
)
diffuse_horz = pd.Series(np.where(dawn, Dh, 0.), index=times).fillna(0.)
assert np.allclose(
testdata['Dif Hz'].where(dusk, 0.), diffuse_horz[1:48], rtol=1e-3
)
# test keyword parameters
irrads2 = clearsky.bird(
zenith, airmass, aod_380nm, aod_500nm, h2o_cm, dni_extra=etr
)
Eb2, Ebh2, Gh2, Dh2 = (irrads2[_] for _ in field_names)
clearsky_path = os.path.dirname(os.path.abspath(__file__))
pvlib_path = os.path.dirname(clearsky_path)
data_path = os.path.join(pvlib_path, 'data', 'BIRD_08_16_2012_patm.csv')
testdata2 = pd.read_csv(data_path, usecols=range(1, 26), header=1).dropna()
testdata2.index = times[1:48]
direct_beam2 = pd.Series(np.where(dawn, Eb2, 0.), index=times).fillna(0.)
assert np.allclose(
testdata2['Direct Beam'].where(dusk, 0.), direct_beam2[1:48], rtol=1e-3
)
direct_horz2 = pd.Series(np.where(dawn, Ebh2, 0.), index=times).fillna(0.)
assert np.allclose(
testdata2['Direct Hz'].where(dusk, 0.), direct_horz2[1:48], rtol=1e-3
)
global_horz2 = pd.Series(np.where(dawn, Gh2, 0.), index=times).fillna(0.)
assert np.allclose(
testdata2['Global Hz'].where(dusk, 0.), global_horz2[1:48], rtol=1e-3
)
diffuse_horz2 = pd.Series(np.where(dawn, Dh2, 0.), index=times).fillna(0.)
assert np.allclose(
testdata2['Dif Hz'].where(dusk, 0.), diffuse_horz2[1:48], rtol=1e-3
)
# test scalars just at noon
# XXX: calculations start at 12am so noon is at index = 12
irrads3 = clearsky.bird(
zenith[12], airmass[12], aod_380nm, aod_500nm, h2o_cm, dni_extra=etr[12]
)
Eb3, Ebh3, Gh3, Dh3 = (irrads3[_] for _ in field_names)
# XXX: testdata starts at 1am so noon is at index = 11
np.allclose(
[Eb3, Ebh3, Gh3, Dh3],
testdata2[['Direct Beam', 'Direct Hz', 'Global Hz', 'Dif Hz']].iloc[11],
rtol=1e-3)
return pd.DataFrame({'Eb': Eb, 'Ebh': Ebh, 'Gh': Gh, 'Dh': Dh}, index=times)
def basic_chain(times, latitude, longitude,
module_parameters, inverter_parameters,
irradiance=None, weather=None,
surface_tilt=None, surface_azimuth=None,
orientation_strategy=None,
transposition_model='haydavies',
solar_position_method='nrel_numpy',
airmass_model='kastenyoung1989',
altitude=None, pressure=None,
**kwargs):
"""
An experimental function that computes all of the modeling steps
necessary for calculating power or energy for a PV system at a given
location.
Parameters
----------
times : DatetimeIndex
Times at which to evaluate the model.
latitude : float.
Positive is north of the equator.
Use decimal degrees notation.
longitude : float.
Positive is east of the prime meridian.
Use decimal degrees notation.
module_parameters : None, dict or Series
Module parameters as defined by the SAPM.
inverter_parameters : None, dict or Series
Inverter parameters as defined by the CEC.
irradiance : None or DataFrame
If None, calculates clear sky data.
Columns must be 'dni', 'ghi', 'dhi'.
weather : None or DataFrame
If None, assumes air temperature is 20 C and
wind speed is 0 m/s.
Columns must be 'wind_speed', 'temp_air'.
surface_tilt : float or Series
Surface tilt angles in decimal degrees.
The tilt angle is defined as degrees from horizontal
(e.g. surface facing up = 0, surface facing horizon = 90)
surface_azimuth : float or Series
Surface azimuth angles in decimal degrees.
The azimuth convention is defined
as degrees east of north
(North=0, South=180, East=90, West=270).
orientation_strategy : None or str
The strategy for aligning the modules.
If not None, sets the ``surface_azimuth`` and ``surface_tilt``
properties of the ``system``.
transposition_model : str
Passed to system.get_irradiance.
solar_position_method : str
Passed to location.get_solarposition.
airmass_model : str
Passed to location.get_airmass.
altitude : None or float
If None, computed from pressure. Assumed to be 0 m
if pressure is also None.
pressure : None or float
If None, computed from altitude. Assumed to be 101325 Pa
if altitude is also None.
**kwargs
Arbitrary keyword arguments.
See code for details.
Returns
-------
output : (dc, ac)
Tuple of DC power (with SAPM parameters) (DataFrame) and AC
power (Series).
"""
# use surface_tilt and surface_azimuth if provided,
# otherwise set them using the orientation_strategy
if surface_tilt is not None and surface_azimuth is not None:
pass
elif orientation_strategy is not None:
surface_tilt, surface_azimuth = \
get_orientation(orientation_strategy, latitude=latitude)
else:
raise ValueError('orientation_strategy or surface_tilt and ' +
'surface_azimuth must be provided')
times = times
if altitude is None and pressure is None:
altitude = 0.
pressure = 101325.
elif altitude is None:
altitude = atmosphere.pres2alt(pressure)
elif pressure is None:
pressure = atmosphere.alt2pres(altitude)
solar_position = solarposition.get_solarposition(times, latitude,
longitude,
altitude=altitude,
pressure=pressure,
**kwargs)
# possible error with using apparent zenith with some models
airmass = atmosphere.relativeairmass(solar_position['apparent_zenith'],
model=airmass_model)
airmass = atmosphere.absoluteairmass(airmass, pressure)
dni_extra = pvlib.irradiance.extraradiation(solar_position.index)
dni_extra = pd.Series(dni_extra, index=solar_position.index)
aoi = pvlib.irradiance.aoi(surface_tilt, surface_azimuth,
solar_position['apparent_zenith'],
solar_position['azimuth'])
if irradiance is None:
irradiance = clearsky.ineichen(
solar_position.index,
latitude,
longitude,
zenith_data=solar_position['apparent_zenith'],
airmass_data=airmass,
altitude=altitude)
total_irrad = pvlib.irradiance.total_irrad(
surface_tilt,
surface_azimuth,
solar_position['apparent_zenith'],
solar_position['azimuth'],
irradiance['dni'],
irradiance['ghi'],
irradiance['dhi'],
model=transposition_model,
dni_extra=dni_extra)
if weather is None:
weather = {'wind_speed': 0, 'temp_air': 20}
temps = pvsystem.sapm_celltemp(total_irrad['poa_global'],
weather['wind_speed'],
weather['temp_air'])
dc = pvsystem.sapm(module_parameters, total_irrad['poa_direct'],
total_irrad['poa_diffuse'],
temps['temp_cell'],
airmass,
aoi)
ac = pvsystem.snlinverter(inverter_parameters, dc['v_mp'], dc['p_mp'])
return dc, ac
def perez_diffuse_luminance(timestamps, array_tilt, array_azimuth,
solar_zenith, solar_azimuth, dni, dhi):
"""
Function used to calculate the luminance and the view factor terms from the
Perez diffuse light transposition model, as implemented in the
``pvlib-python`` library.
This function was custom made to allow the calculation of the circumsolar
component on the back surface as well. Otherwise, the ``pvlib``
implementation would ignore it.
:param array-like timestamps: simulation timestamps
:param array-like array_tilt: pv module tilt angles
:param array-like array_azimuth: pv array azimuth angles
:param array-like solar_zenith: solar zenith angles
:param array-like solar_azimuth: solar azimuth angles
:param array-like dni: values for direct normal irradiance
:param array-like dhi: values for diffuse horizontal irradiance
:return: ``df_inputs``, dataframe with the following columns:
['solar_zenith', 'solar_azimuth', 'array_tilt', 'array_azimuth', 'dhi',
'dni', 'vf_horizon', 'vf_circumsolar', 'vf_isotropic',
'luminance_horizon', 'luminance_circumsolar', 'luminance_isotropic',
'poa_isotropic', 'poa_circumsolar', 'poa_horizon', 'poa_total_diffuse']
:rtype: class:`pandas.DataFrame`
"""
# Create a dataframe to help filtering on all arrays
df_inputs = pd.DataFrame(
{
'array_tilt': array_tilt,
'array_azimuth': array_azimuth,
'solar_zenith': solar_zenith,
'solar_azimuth': solar_azimuth,
'dni': dni,
'dhi': dhi
},
index=pd.DatetimeIndex(timestamps))
dni_et = irradiance.extraradiation(df_inputs.index.dayofyear)
am = atmosphere.relativeairmass(df_inputs.solar_zenith)
# Need to treat the case when the sun is hitting the back surface of pvrow
aoi_proj = aoi_projection(df_inputs.array_tilt, df_inputs.array_azimuth,
df_inputs.solar_zenith, df_inputs.solar_azimuth)
sun_hitting_back_surface = ((aoi_proj < 0) &
(df_inputs.solar_zenith <= 90))
df_inputs_back_surface = df_inputs.loc[sun_hitting_back_surface]
# Reverse the surface normal to switch to back-surface circumsolar calc
df_inputs_back_surface.loc[:, 'array_azimuth'] -= 180.
df_inputs_back_surface.loc[:, 'array_azimuth'] = np.mod(
df_inputs_back_surface.loc[:, 'array_azimuth'], 360.)
df_inputs_back_surface.loc[:, 'array_tilt'] = (
180. - df_inputs_back_surface.array_tilt)
if df_inputs_back_surface.shape[0] > 0:
# Use recursion to calculate circumsolar luminance for back surface
df_inputs_back_surface = perez_diffuse_luminance(
*breakup_df_inputs(df_inputs_back_surface))
# Calculate Perez diffuse components
diffuse_poa, components = irradiance.perez(df_inputs.array_tilt,
df_inputs.array_azimuth,
df_inputs.dhi,
df_inputs.dni,
dni_et,
df_inputs.solar_zenith,
df_inputs.solar_azimuth,
am,
return_components=True)
# Calculate Perez view factors:
a = aoi_projection(df_inputs.array_tilt, df_inputs.array_azimuth,
df_inputs.solar_zenith, df_inputs.solar_azimuth)
a = np.maximum(a, 0)
b = cosd(df_inputs.solar_zenith)
b = np.maximum(b, cosd(85))
vf_perez = pd.DataFrame(
np.array([
sind(df_inputs.array_tilt), a / b,
(1. + cosd(df_inputs.array_tilt)) / 2.
]).T,
index=df_inputs.index,
columns=['vf_horizon', 'vf_circumsolar', 'vf_isotropic'])
# Calculate diffuse luminance
luminance = pd.DataFrame(np.array([
components['horizon'] / vf_perez['vf_horizon'],
components['circumsolar'] / vf_perez['vf_circumsolar'],
components['isotropic'] / vf_perez['vf_isotropic']
]).T,
index=df_inputs.index,
columns=[
'luminance_horizon', 'luminance_circumsolar',
'luminance_isotropic'
])
luminance.loc[diffuse_poa == 0, :] = 0.
# Format components column names
components = components.rename(
columns={
'isotropic': 'poa_isotropic',
'circumsolar': 'poa_circumsolar',
'horizon': 'poa_horizon'
})
df_inputs = pd.concat(
[df_inputs, components, vf_perez, luminance, diffuse_poa],
axis=1,
join='outer')
df_inputs = df_inputs.rename(columns={0: 'poa_total_diffuse'})
# Adjust the circumsolar luminance when it hits the back surface
if df_inputs_back_surface.shape[0] > 0:
df_inputs.loc[sun_hitting_back_surface, 'luminance_circumsolar'] = (
df_inputs_back_surface.loc[:, 'luminance_circumsolar'])
return df_inputs
def test_airmass(model):
out = atmosphere.relativeairmass(ephem_data['zenith'], model)
assert isinstance(out, pd.Series)
out = atmosphere.relativeairmass(ephem_data['zenith'].values, model)
assert isinstance(out, np.ndarray)
from pvlib import pvsystem
from pvlib import clearsky
from pvlib import irradiance
from pvlib import atmosphere
from pvlib import solarposition
from pvlib.location import Location
tus = Location(32.2, -111, 'US/Arizona', 700, 'Tucson')
times = pd.date_range(start=datetime.datetime(2014,1,1),
end=datetime.datetime(2014,1,2), freq='1Min')
ephem_data = solarposition.get_solarposition(times, tus, method='pyephem')
irrad_data = clearsky.ineichen(times, tus, linke_turbidity=3,
solarposition_method='pyephem')
aoi = irradiance.aoi(0, 0, ephem_data['apparent_zenith'],
ephem_data['apparent_azimuth'])
am = atmosphere.relativeairmass(ephem_data.apparent_zenith)
meta = {'latitude': 37.8,
'longitude': -122.3,
'altitude': 10,
'Name': 'Oakland',
'State': 'CA',
'TZ': -8}
pvlib_abspath = os.path.dirname(os.path.abspath(inspect.getfile(tmy)))
tmy3_testfile = os.path.join(pvlib_abspath, 'data', '703165TY.csv')
tmy2_testfile = os.path.join(pvlib_abspath, 'data', '12839.tm2')
tmy3_data, tmy3_metadata = tmy.readtmy3(tmy3_testfile)
tmy2_data, tmy2_metadata = tmy.readtmy2(tmy2_testfile)
def test_airmass_invalid():
atmosphere.relativeairmass(ephem_data["zenith"], "invalid")
