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 ghi is None:
irr = clear_sky_irradiances(dates=df.index, latitude=latitude,
longitude=longitude, altitude=altitude,
timezone=timezone)
df = pandas.concat([df, irr], axis=1)
elif 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 daily_diffuse_fraction(ghi, times, latitude):
""" estimate the diffuse fraction using daily averages of global horizontal irradiance"""
Io = sun_extraradiation(times)
dayofyear = times.tz_convert('UTC').dayofyear
year = times.tz_convert('UTC').year
So = Io * sinel_integral(dayofyear, year, latitude)
RsRso = ghi / So
return numpy.where(
RsRso <= 0.07, 1,
numpy.where(RsRso <= 0.35, 1 - 2.3 * (RsRso - 0.07)**2,
numpy.where(RsRso <= 0.75, 1.33 - 1.46 * RsRso, 0.23)))
def clear_sky_irradiances(dates=None,
daydate=_daydate,
longitude=_longitude,
latitude=_latitude,
altitude=_altitude,
timezone=_timezone):
""" Estimate components of sky irradiance for clear sy 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:
GHI is computed after Haurwitz (1945) and DNI after Meinel (1976)
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,
daydate=daydate,
latitude=latitude,
longitude=longitude,
altitude=altitude,
timezone=timezone)
df['am'] = air_mass(df['zenith'], altitude)
df['dni_extra'] = sun_extraradiation(df.index)
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'])
return clearsky.loc[:, ['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:
Spitters CJT, Toussaint HAJM, Goudriaan J (1986) Separating the diffuse
and direct component of global radiation and its implications for
modeling canopy photosynthesis.Part I.
Components of incoming radiation.
Agricultural and Forest Meteorology 38: 217-229.
"""
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
Io = sun_extraradiation(df.index)
costheta = numpy.sin(numpy.radians(df.elevation))
So = Io * costheta
RsRso = df.ghi / So
R = 0.847 - 1.61 * costheta + 1.04 * costheta * costheta
K = (1.47 - R) / 1.66
RdRs = numpy.where(
RsRso <= 0.22, 1,
numpy.where(RsRso <= 0.35, 1 - 6.4 * (RsRso - 0.22)**2,
numpy.where(RsRso <= K, 1.47 - 1.66 * RsRso, R)))
df['dhi'] = df.ghi * RdRs
df['dni'] = normal_irradiance(df['ghi'] - df['dhi'], df['elevation'])
return df.loc[:, ('ghi', 'dhi', 'dni')]