def test_alternative_sun_position():
sun = sun_position()
sune = sun_position_ephem()
suna = sun_position_astk()
# ephem is equivalent < 1%
numpy.testing.assert_allclose(sune, sun, rtol=0.01)
# astk is equivalent <5 %
numpy.testing.assert_allclose(suna, sun, rtol=0.05)
def sun_sources(irradiance=1,
dates=None,
daydate=_daydate,
longitude=_longitude,
latitude=_latitude,
altitude=_altitude,
timezone=_timezone):
""" Light sources representing the sun under clear sky conditions
Args:
irradiance: (float) sum of horizontal irradiance of sources.
Using irradiance=1 (default) yields relative contribution of sources.
If None, clear sky sun horizontal irradiance predicted by
Perez/Ineichen model is used.
dates: A pandas datetime index (as generated by pandas.date_range). If
None, hourly values for daydate are used.
daydate: (str) yyyy-mm-dd (not used if dates is not None).
longitude: (float) in degrees
latitude: (float) in degrees
altitude: (float) in meter
timezone:(str) the time zone (not used if dates are already localised)
Returns:
elevation (degrees), azimuth (degrees, from North positive clockwise)
and horizontal irradiance of sources
"""
c_sky = clear_sky_irradiances(dates=dates,
daydate=daydate,
longitude=longitude,
latitude=latitude,
altitude=altitude,
timezone=timezone)
sun_irradiance = c_sky['ghi'] - c_sky['dhi']
if irradiance is not None:
sun_irradiance /= sum(sun_irradiance)
sun_irradiance *= irradiance
# Sr = (1 -cos(cone half angle)) * 2 * pi, frac = Sr / 2 / pi
# fsun = 1 - numpy.cos(numpy.radians(.53 / 2))
sun = sun_position(dates=dates,
daydate=daydate,
latitude=latitude,
longitude=longitude,
altitude=altitude,
timezone=timezone)
return sun['elevation'].values, sun[
'azimuth'].values, sun_irradiance.values
def clear_sky_irradiances(dates=None,
daydate=_daydate,
longitude=_longitude,
latitude=_latitude,
altitude=_altitude,
timezone=_timezone):
""" Estimate component of sky irradiance for clear sky conditions
Args:
dates: A pandas datetime index (as generated by pandas.date_range). If
None, daydate is used.
daydate: (str) yyyy-mm-dd (not used if dates is not None).
longitude: (float) in degrees
latitude: (float) in degrees
altitude: (float) in meter
timezone:(str) the time zone (not used if dates are already localised)
Returns:
a pandas dataframe with global horizontal irradiance, direct normal
irradiance and diffuse horizontal irradiance.
Details:
P. Ineichen and R. Perez, "A New airmass independent formulation for
the Linke turbidity coefficient", Solar Energy, vol 73, pp. 151-157,
2002
"""
df = sun_position(dates=dates,
daydate=daydate,
latitude=latitude,
longitude=longitude,
altitude=altitude,
timezone=timezone)
tl = pvlib.clearsky.lookup_linke_turbidity(df.index, latitude, longitude)
am = air_mass(df['zenith'], altitude)
dni_extra = sun_extraradiation(df.index)
clearsky = pvlib.clearsky.ineichen(df['zenith'],
am,
tl,
dni_extra=dni_extra,
altitude=altitude)
clearsky = pandas.concat([df, clearsky], axis=1)
return clearsky.loc[:, ['ghi', 'dni', 'dhi']]
def actual_sky_irradiances(dates, ghi, dhi=None, Tdew=None, longitude=_longitude, latitude=_latitude, altitude=_altitude, method='dirint'):
""" derive a sky irradiance dataframe from actual weather data"""
df = sun_position(dates=dates, latitude=latitude, longitude=longitude, altitude=altitude, filter_night=False)
df['am'] = air_mass(df['zenith'], altitude)
df['dni_extra'] = sun_extraradiation(df.index)
if dhi is None:
pressure = pvlib.atmosphere.alt2pres(altitude)
dhi = diffuse_horizontal_irradiance(ghi, df['elevation'], dates, pressure=pressure, temp_dew=Tdew, method=method)
df['ghi'] = ghi
df['dhi'] = dhi
el = numpy.radians(df['elevation'])
df['dni'] = (df['ghi'] - df['dhi']) / numpy.sin(el)
df['brightness'] = brightness(df['am'], df['dhi'], df['dni_extra'])
df['clearness'] = clearness(df['dni'], df['dhi'], df['zenith'])
return df.loc[(df['elevation'] > 0) & (df['ghi'] > 0) , ['azimuth', 'zenith', 'elevation', 'am', 'dni_extra', 'clearness', 'brightness', 'ghi', 'dni', 'dhi' ]]
def clear_sky_irradiances(dates=_dates, longitude=_longitude, latitude=_latitude, altitude=_altitude, timezone=_timezone, method = 'ineichen'):
""" Estimate components of sky irradiance for clear sy conditions at a given location
Args:
dates:
longitude:
latitude:
altitude:
timezone:
Returns:
a pandas dataframe with DNI, GHI and DHI.
Details:
the Perez / Ineichen model (2002) is used, except if pvlib is not available.
In the later case, GHI is computed after Haurwitz (1945) and DNI after Meinel (1976)
P. Ineichen and R. Perez, "A New airmass independent formulation for
the Linke turbidity coefficient", Solar Energy, vol 73, pp. 151-157, 2002
B. Haurwitz, "Insolation in Relation to Cloudiness and Cloud
Density," Journal of Meteorology, vol. 2, pp. 154-166, 1945.
A. B. Meinel and M. P. Meinel, Applied solar energy.
Reading, MA: Addison-Wesley Publishing Co., 1976
"""
df = sun_position(dates=dates, latitude=latitude, longitude=longitude, altitude=altitude, timezone=timezone)
df['am'] = air_mass(df['zenith'], altitude)
df['dni_extra'] = sun_extraradiation(df.index)
if method == 'ineichen' and pvlib_installed:
tl = pvlib.clearsky.lookup_linke_turbidity(df.index, latitude, longitude)
clearsky = pvlib.clearsky.ineichen(df['zenith'], df['am'], tl, dni_extra = df['dni_extra'], altitude = altitude)
clearsky = pandas.concat([df, clearsky], axis=1)
else:
clearsky = df
z = numpy.radians(df['zenith'])
clearsky['ghi'] = 1098 * numpy.cos(z) * numpy.exp(-0.057 / numpy.cos(z))
clearsky['dni'] = df['dni_extra'] * numpy.power(0.7, numpy.power(df['am'], 0.678))
clearsky['dhi'] = clearsky['ghi'] - horizontal_irradiance(clearsky['dni'], df['elevation'])
clearsky['brightness'] = brightness(clearsky['am'], clearsky['dhi'], clearsky['dni_extra'])
clearsky['clearness'] = clearness(clearsky['dni'], clearsky['dhi'], clearsky['zenith'])
return clearsky.loc[:,['azimuth', 'zenith', 'elevation', 'am', 'dni_extra', 'clearness', 'brightness', 'ghi', 'dni', 'dhi' ]]
def sun_path(self, seq):
""" Return position of the sun corresponing to a sequence of date
"""
return sun_position(seq, timezone='utc')
def test_sun_position():
sun = sun_position()
assert len(sun) == 15
def sun_path(self, seq):
""" Return position of the sun corresponing to a sequence of date
"""
return sun_position(seq, timezone='utc')
def sky_sources(sky_type='soc',
irradiance=1,
turtle_sectors=46,
dates=None,
daydate=_daydate,
longitude=_longitude,
latitude=_latitude,
altitude=_altitude,
timezone=_timezone):
""" Light sources representing standard cie sky types in 46 directions
Args:
sky_type:(str) type of sky luminance model. One of :
'soc' (standard overcast sky),
'uoc' (uniform overcast sky)
'clear_sky' (standard clear sky)
irradiance: (float) sum of horizontal irradiance of all sources. If None
diffuse horizontal clear_sky irradiance are used for clear_sky type and
20% attenuated clear_sky global horizontal irradiances are used for
soc and uoc types.
turtle_sectors: (int) the minimal number of sectors to be used for sky discretisation
dates: A pandas datetime index (as generated by pandas.date_range). If
None, hourly values for daydate are used.
daydate: (str) yyyy-mm-dd (not used if dates is not None).
longitude: (float) in degrees
latitude: (float) in degrees
altitude: (float) in meter
timezone:(str) the time zone (not used if dates are already localised)
Returns:
elevation (degrees), azimuth (degrees, from North positive clockwise),
and horizontal irradiance of sources
"""
source_elevation, source_azimuth, source_fraction = sky_discretisation(
turtle_sectors)
if sky_type == 'soc' or sky_type == 'uoc':
radiance = sky_radiance_distribution(source_elevation,
source_azimuth,
source_fraction,
sky_type=sky_type)
source_irradiance = horizontal_irradiance(radiance, source_elevation)
if irradiance is None:
sky_irradiance = clear_sky_irradiances(dates=dates,
daydate=daydate,
longitude=longitude,
latitude=latitude,
altitude=altitude,
timezone=timezone)
irradiance = sum(sky_irradiance['ghi']) * 0.2
elif sky_type == 'clear_sky':
sun = sun_position(dates=dates,
daydate=daydate,
latitude=latitude,
longitude=longitude,
altitude=altitude,
timezone=timezone)
c_sky = clear_sky_irradiances(dates=dates,
daydate=daydate,
longitude=longitude,
latitude=latitude,
altitude=altitude,
timezone=timezone)
c_sky = pandas.concat([sun, c_sky], axis=1)
if irradiance is None:
irradiance = sum(c_sky['dhi'])
# temporal weigths : use dhi (diffuse horizontal irradiance)
c_sky['wsky'] = c_sky['dhi'] / sum(c_sky['dhi'])
source_irradiance = numpy.zeros_like(source_fraction)
for i, row in c_sky.iterrows():
rad = sky_radiance_distribution(source_elevation,
source_azimuth,
source_fraction,
sky_type='clear_sky',
sun_elevation=row['elevation'],
sun_azimuth=row['azimuth'],
avoid_sun=True)
source_irradiance += (
horizontal_irradiance(rad, source_elevation) * row['wsky'])
else:
raise ValueError('unknown type: ' + sky_type +
' (should be one of uoc, soc, clear_sky')
source_irradiance /= sum(source_irradiance)
source_irradiance *= irradiance
return source_elevation, source_azimuth, source_irradiance
def sky_irradiances(dates=None,
daydate=_daydate,
ghi=None,
dhi=None,
attenuation=None,
pressure=101325,
temp_dew=None,
longitude=_longitude,
latitude=_latitude,
altitude=_altitude,
timezone=_timezone):
""" Estimate variables related to sky irradiance.
Args:
dates: A pandas datetime index (as generated by pandas.date_range). If
None, daydate is used.
daydate: (str) yyyy-mm-dd (not used if dates is not None).
ghi: (array_like) : global horizontal irradiance (W. m-2).If None
(default) clear_sky irradiance are used
dhi: (array-like): diffuse horizontal irradiance
attenuation: (float) a attenuation factor for ghi (actual_ghi =
attenuation * ghi). If None (default), no attenuation is applied. If
dhi is not None, this parameter is not taken into account.
pressure: the site pressure (Pa) (for dirint model)
temp_dew: the dew point temperature (dirint model)
longitude: (float) in degrees
latitude: (float) in degrees
altitude: (float) in meter
timezone:(str) the time zone (not used if dates are already localised)
Returns:
a pandas dataframe with azimuth, zenital and elevation angle of the sun,
clearness and brightness indices, global horizontal irradiance, direct
normal irradiance and diffuse horizontal irradiance of the sky.
"""
df = sun_position(dates=dates,
daydate=daydate,
latitude=latitude,
longitude=longitude,
altitude=altitude,
timezone=timezone)
if len(df) < 1: # night
if ghi is not None: # twilight conditions (sun_el < 0, ghi > 0)
df = sun_position(dates=dates,
daydate=daydate,
latitude=latitude,
longitude=longitude,
altitude=altitude,
timezone=timezone,
filter_night=False)
df['ghi'] = ghi
df['dhi'] = ghi
df['dni'] = 0
df['clearness'] = None
df['brightness'] = None
df = df.loc[df.ghi > 0, :]
else:
df['ghi'] = 0
df['dhi'] = 0
df['dni'] = 0
df['clearness'] = None
df['brightness'] = None
else: # day
if ghi is None or dhi is None:
irr = actual_sky_irradiances(dates=df.index,
ghi=ghi,
attenuation=attenuation,
pressure=pressure,
temp_dew=temp_dew,
latitude=latitude,
longitude=longitude,
altitude=altitude,
timezone=timezone)
df = pandas.concat([df, irr], axis=1)
else:
df['ghi'] = ghi
df['dhi'] = dhi
df['dni'] = normal_irradiance(
numpy.array(ghi) - numpy.array(dhi), df.elevation)
am = air_mass(df['zenith'], altitude)
dni_extra = sun_extraradiation(df.index)
df['brightness'] = brightness(am, df['dhi'], dni_extra)
df['clearness'] = clearness(df['dni'], df['dhi'], df['zenith'])
return df.loc[:, [
'azimuth', 'zenith', 'elevation', 'clearness', 'brightness', 'ghi',
'dni', 'dhi'
]]
def actual_sky_irradiances(dates=None,
daydate=_daydate,
ghi=None,
attenuation=None,
pressure=101325,
temp_dew=None,
longitude=_longitude,
latitude=_latitude,
altitude=_altitude,
timezone=_timezone):
""" Estimate component of sky irradiances from measured actual global
horizontal irradiance or attenuated clearsky conditions.
Args:
dates: A pandas datetime index (as generated by pandas.date_range). If
None, daydate is used.
daydate: (str) yyyy-mm-dd (not used if dates is not None).
ghi: (array_like) : global horizontal irradiance (W. m-2).If None
(default) clear_sky irradiance are used
attenuation: (float) a attenuation factor for ghi (actual_ghi =
attenuation * ghi). If None (default), no attenuation is applied
pressure: the site pressure (Pa) (for dirint model)
temp_dew: the dew point temperature (dirint model)
longitude: (float) in degrees
latitude: (float) in degrees
altitude: (float) in meter
timezone:(str) the time zone (not used if dates are already localised)
Returns:
a pandas dataframe with global horizontal irradiance, direct normal
irradiance and diffuse horizontal irradiance.
Details:
Perez, R., P. Ineichen, E. Maxwell, R. Seals and A. Zelenka, (1992).
Dynamic Global-to-Direct Irradiance Conversion Models.
ASHRAE Transactions-Research Series, pp. 354-369
"""
df = sun_position(dates=dates,
daydate=daydate,
latitude=latitude,
longitude=longitude,
altitude=altitude,
timezone=timezone)
if ghi is None:
cs = clear_sky_irradiances(dates=df.index,
latitude=latitude,
longitude=longitude,
altitude=altitude,
timezone=timezone)
ghi = cs['ghi']
df['ghi'] = ghi
if attenuation is not None:
df.ghi *= attenuation
df['dni'] = pvlib.irradiance.dirint(df.ghi,
90 - df.elevation,
df.index,
pressure=pressure,
temp_dew=temp_dew)
df['dhi'] = df.ghi - horizontal_irradiance(df.dni, df.elevation)
return df.loc[:, ('ghi', 'dhi', 'dni')]
