def pvl_clearsky_ineichen(Time, Location, LinkeTurbidity=-999):
'''
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 Linke turbidity
provided by SoDa [4, 5].
Parameters
----------
Time : Dataframe.index
A timezone aware pandas dataframe index.
Location : dict
latitude
vector or scalar latitude in decimal degrees (positive is
northern hemisphere)
longitude
vector or scalar longitude in decimal degrees (positive is
east of prime meridian)
altitude
an optional component of the Location struct, not
used in the ephemeris code directly, but it may be used to calculate
standard site pressure (see pvl_alt2pres function)
TZ
Time Zone offset from UTC
Other Parameters
----------------
LinkeTurbidityInput : Optional, float or DataFrame
An optional input to provide your own Linke
turbidity. If this input is omitted, the default Linke turbidity
maps will be used. LinkeTurbidityInput may be a float or
dataframe of Linke turbidities. If dataframe is provided, the same
turbidity will be used for all time/location sets. If a dataframe is
provided, it must be of the same size as any time/location dataframes
and each element of the dataframe corresponds to any time and location
elements.
Returns
-------
ClearSkyGHI : Dataframe
the modeled global horizonal irradiance in W/m^2 provided
by the Ineichen clear-sky model.
ClearSkyDNI : Dataframe
the modeled direct normal irradiance in W/m^2 provided
by the Ineichen clear-sky model.
ClearSkyDHI : Dataframe
the calculated diffuse horizonal irradiance in W/m^2
provided by the Ineichen clear-sky model.
Notes
-----
This implementation of the Ineichen model requires a number of other
PV_LIB functions including pvl_ephemeris, pvl_date2doy,
pvl_extraradiation, pvl_absoluteairmass, pvl_relativeairmass, and
pvl_alt2pres. It also requires the file "LinkeTurbidities.mat" to be
in the working directory. If you are using pvl_ineichen
in a loop, it may be faster to load LinkeTurbidities.mat outside of
the loop and feed it into pvl_ineichen as a variable, rather than
having pvl_ineichen open the file each time it is called (or utilize
column vectors of time/location instead of a loop).
Initial implementation of this algorithm by Matthew Reno.
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.
See Also
--------
pvl_maketimestruct
pvl_makelocationstruct
pvl_ephemeris
pvl_haurwitz
'''
Vars = locals()
Expect = {'Time': (''), 'Location': (''), 'LinkeTurbidity': ('optional')}
var = pvl_tools.Parse(Vars, Expect)
I0 = pvl_extraradiation.pvl_extraradiation(var.Time.dayofyear)
__, __, ApparentSunElevation, __, __ = pvl_ephemeris.pvl_ephemeris(
var.Time, var.Location,
pvl_alt2pres.pvl_alt2pres(var.Location['altitude'])) # nargout=4
ApparentZenith = 90 - ApparentSunElevation
ApparentZenith[ApparentZenith >= 90] = 90
if LinkeTurbidity == -999:
# 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.
mat = scipy.io.loadmat('LinkeTurbidities.mat')
LinkeTurbidity = mat['LinkeTurbidity']
LatitudeIndex = np.round_(
LinearlyScale(Location['latitude'], 90, -90, 1, 2160))
LongitudeIndex = np.round_(
LinearlyScale(Location['longitude'], -180, 180, 1, 4320))
g = LinkeTurbidity[LatitudeIndex][LongitudeIndex]
ApplyMonth = lambda x: g[x[0] - 1]
LT = pd.DataFrame(Time.month)
LT.index = Time
LT = LT.apply(ApplyMonth, axis=1)
TL = LT / float(20)
else:
TL = var.LinkeTurbidity
# Get the absolute airmass assuming standard local pressure (per
# pvl_alt2pres) using Kasten and Young's 1989 formula for airmass.
AMabsolute = pvl_absoluteairmass.pvl_absoluteairmass(
AMrelative=pvl_relativeairmass.pvl_relativeairmass(
ApparentZenith, model='kastenyoung1989'),
Pressure=pvl_alt2pres.pvl_alt2pres(var.Location['altitude']))
fh1 = np.exp(var.Location['altitude'] * ((-1 / 8000)))
fh2 = np.exp(var.Location['altitude'] * ((-1 / 1250)))
cg1 = (5.09e-05 * (var.Location['altitude']) + 0.868)
cg2 = 3.92e-05 * (var.Location['altitude']) + 0.0387
# 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. 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)).
ClearSkyGHI = cg1 * (I0) * (pvl_tools.cosd(ApparentZenith)) * (np.exp(
-cg2 * (AMabsolute) *
((fh1 + fh2 * ((TL - 1)))))) * (np.exp(0.01 * ((AMabsolute)**(1.8))))
ClearSkyGHI[ClearSkyGHI < 0] = 0
b = 0.664 + 0.163 / fh1
BncI = b * (I0) * (np.exp(-0.09 * (AMabsolute) * ((TL - 1))))
ClearSkyDNI = np.min(
BncI,
ClearSkyGHI * ((1 - (0.1 - 0.2 * (np.exp(-TL))) /
(0.1 + 0.882 / fh1))) / pvl_tools.cosd(ApparentZenith))
#ClearSkyDNI=ClearSkyGHI*((1 - (0.1 - 0.2*(np.exp(- TL))) / (0.1 + 0.882 / fh1))) / pvl_tools.cosd(ApparentZenith)
ClearSkyDHI = ClearSkyGHI - ClearSkyDNI * (pvl_tools.cosd(ApparentZenith))
return ClearSkyGHI, ClearSkyDNI, ClearSkyDHI, BncI
def pvl_clearsky_ineichen(Time,Location,LinkeTurbidity=-999):
'''
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 Linke turbidity
provided by SoDa [4, 5].
Parameters
----------
Time : Dataframe.index
A timezone aware pandas dataframe index.
Location : dict
latitude
vector or scalar latitude in decimal degrees (positive is
northern hemisphere)
longitude
vector or scalar longitude in decimal degrees (positive is
east of prime meridian)
altitude
an optional component of the Location struct, not
used in the ephemeris code directly, but it may be used to calculate
standard site pressure (see pvl_alt2pres function)
TZ
Time Zone offset from UTC
Other Parameters
----------------
LinkeTurbidityInput : Optional, float or DataFrame
An optional input to provide your own Linke
turbidity. If this input is omitted, the default Linke turbidity
maps will be used. LinkeTurbidityInput may be a float or
dataframe of Linke turbidities. If dataframe is provided, the same
turbidity will be used for all time/location sets. If a dataframe is
provided, it must be of the same size as any time/location dataframes
and each element of the dataframe corresponds to any time and location
elements.
Returns
-------
ClearSkyGHI : Dataframe
the modeled global horizonal irradiance in W/m^2 provided
by the Ineichen clear-sky model.
ClearSkyDNI : Dataframe
the modeled direct normal irradiance in W/m^2 provided
by the Ineichen clear-sky model.
ClearSkyDHI : Dataframe
the calculated diffuse horizonal irradiance in W/m^2
provided by the Ineichen clear-sky model.
Notes
-----
This implementation of the Ineichen model requires a number of other
PV_LIB functions including pvl_ephemeris, pvl_date2doy,
pvl_extraradiation, pvl_absoluteairmass, pvl_relativeairmass, and
pvl_alt2pres. It also requires the file "LinkeTurbidities.mat" to be
in the working directory. If you are using pvl_ineichen
in a loop, it may be faster to load LinkeTurbidities.mat outside of
the loop and feed it into pvl_ineichen as a variable, rather than
having pvl_ineichen open the file each time it is called (or utilize
column vectors of time/location instead of a loop).
Initial implementation of this algorithm by Matthew Reno.
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.
See Also
--------
pvl_maketimestruct
pvl_makelocationstruct
pvl_ephemeris
pvl_haurwitz
'''
Vars=locals()
Expect={'Time':(''),
'Location':(''),
'LinkeTurbidity':('optional')}
var=pvl_tools.Parse(Vars,Expect)
I0=pvl_extraradiation.pvl_extraradiation(var.Time.dayofyear)
__,__,ApparentSunElevation,__,__=pvl_ephemeris.pvl_ephemeris(var.Time,var.Location,pvl_alt2pres.pvl_alt2pres(var.Location['altitude'])) # nargout=4
ApparentZenith=90 - ApparentSunElevation
ApparentZenith[ApparentZenith>=90]=90
if LinkeTurbidity==-999:
# 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.
mat = scipy.io.loadmat('LinkeTurbidities.mat')
LinkeTurbidity=mat['LinkeTurbidity']
LatitudeIndex=np.round_(LinearlyScale(Location['latitude'],90,- 90,1,2160))
LongitudeIndex=np.round_(LinearlyScale(Location['longitude'],- 180,180,1,4320))
g=LinkeTurbidity[LatitudeIndex][LongitudeIndex]
ApplyMonth=lambda x:g[x[0]-1]
LT=pd.DataFrame(Time.month)
LT.index=Time
LT=LT.apply(ApplyMonth,axis=1)
TL=LT / float(20)
else:
TL=var.LinkeTurbidity
# Get the absolute airmass assuming standard local pressure (per
# pvl_alt2pres) using Kasten and Young's 1989 formula for airmass.
AMabsolute=pvl_absoluteairmass.pvl_absoluteairmass(AMrelative=pvl_relativeairmass.pvl_relativeairmass(ApparentZenith,model='kastenyoung1989'),Pressure=pvl_alt2pres.pvl_alt2pres(var.Location['altitude']))
fh1=np.exp(var.Location['altitude']*((- 1 / 8000)))
fh2=np.exp(var.Location['altitude']*((- 1 / 1250)))
cg1=(5.09e-05*(var.Location['altitude']) + 0.868)
cg2=3.92e-05*(var.Location['altitude']) + 0.0387
# 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. 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)).
ClearSkyGHI=cg1*(I0)*(pvl_tools.cosd(ApparentZenith))*(np.exp(- cg2*(AMabsolute)*((fh1 + fh2*((TL - 1))))))*(np.exp(0.01*((AMabsolute) ** (1.8))))
ClearSkyGHI[ClearSkyGHI < 0]=0
b=0.664 + 0.163 / fh1
BncI=b*(I0)*(np.exp(- 0.09*(AMabsolute)*((TL - 1))))
ClearSkyDNI=np.min(BncI,ClearSkyGHI*((1 - (0.1 - 0.2*(np.exp(- TL))) / (0.1 + 0.882 / fh1))) / pvl_tools.cosd(ApparentZenith))
#ClearSkyDNI=ClearSkyGHI*((1 - (0.1 - 0.2*(np.exp(- TL))) / (0.1 + 0.882 / fh1))) / pvl_tools.cosd(ApparentZenith)
ClearSkyDHI=ClearSkyGHI - ClearSkyDNI*(pvl_tools.cosd(ApparentZenith))
return ClearSkyGHI,ClearSkyDNI,ClearSkyDHI,BncI
# Out[8]:
# <class 'pandas.tseries.index.DatetimeIndex'>
# [1981-03-26 00:00:00, ..., 1981-03-26 23:00:00]
# Length: 24, Freq: H, Timezone: None
## Get solar angles
# In[9]:
import pvl_ephemeris
reload(pvl_ephemeris)
#Using Ephemeris Calculations
TMY['SunAz'], TMY['SunEl'], TMY['ApparentSunEl'], TMY['SolarTime'], TMY[
'SunZen'] = pvl_ephemeris.pvl_ephemeris(Time=TMY.index, Location=meta)
#Using NRELS SPA Calculations
import pvl_spa
reload(pvl_spa)
TMY['SunAz_spa'], TMY['SunEl_spa'], TMY['SunZen_spa'] = pvl_spa.pvl_spa(
Time=TMY.index, Location=meta)
# In[10]:
clf()
plot(TMY.index, TMY.SunAz)
plot(TMY.index, TMY.SunAz_spa)
plot(TMY.index, TMY.SunEl, label='eph')
plot(TMY.index, TMY.SunEl_spa, label='spa')
legend()
# Out[8]: # <class 'pandas.tseries.index.DatetimeIndex'> # [1981-03-26 00:00:00, ..., 1981-03-26 23:00:00] # Length: 24, Freq: H, Timezone: None ## Get solar angles # In[9]: import pvl_ephemeris reload(pvl_ephemeris) #Using Ephemeris Calculations TMY['SunAz'],TMY['SunEl'],TMY['ApparentSunEl'],TMY['SolarTime'], TMY['SunZen']=pvl_ephemeris.pvl_ephemeris(Time=TMY.index,Location=meta) #Using NRELS SPA Calculations import pvl_spa reload (pvl_spa) TMY['SunAz_spa'],TMY['SunEl_spa'],TMY['SunZen_spa']=pvl_spa.pvl_spa(Time=TMY.index,Location=meta) # In[10]: clf() plot(TMY.index,TMY.SunAz) plot(TMY.index,TMY.SunAz_spa) plot(TMY.index,TMY.SunEl,label='eph') plot(TMY.index,TMY.SunEl_spa,label='spa') legend()
