This tutorial explores some of the functions available in the pvlib
module irradiance.py
.
Authors:
%matplotlib inline
import matplotlib.pyplot as plt
try:
import seaborn as sns
except ImportError:
pass
# built in python modules
import datetime
# python add-ons
import numpy as np
import pandas as pd
import pvlib
Many solar power algorithms start with the irradiance incident on the top of the Earth's atmosphere, often known as the extraterrestrial radiation. pvlib
has three different algorithms to calculate the yearly cycle of the extraterrestrial radiation given the solar constant.
times = pd.date_range('2014-01-01', '2015-01-01', freq='1D')
spencer = pd.Series(pvlib.irradiance.extraradiation(times, method='spencer'), times)
asce = pd.Series(pvlib.irradiance.extraradiation(times, method='asce'), times)
ephem = pvlib.irradiance.extraradiation(times, method='pyephem') # approx 100x slower than the above
spencer.plot(label='spencer')
asce.plot(label='asce')
ephem.plot(label='pyephem')
plt.legend()
plt.ylabel('Extraterrestrial radiation (W/m^2)')
<matplotlib.text.Text at 0x7f582e57a278>
The pyephem
method is probably the most accurate since it uses an external library specifically designed for astronomical position calculations. However, as shown in the plot below, the difference is only +/-2 W/m^2 over the entire year.
et_diff = spencer - ephem
et_diff.plot()
plt.ylabel('spencer-ephem (W/m**2)')
<matplotlib.text.Text at 0x7f582e390710>
You can also control the solar constant.
spencer_1400 = pd.Series(pvlib.irradiance.extraradiation(times, method='spencer', solar_constant=1400), times)
spencer.plot(label='default')
spencer_1400.plot(label='1400')
plt.legend()
plt.title('Impact of solar constant')
plt.ylabel('ET Irradiance (W/m^2)')
<matplotlib.text.Text at 0x7f582e325cf8>
pvlib
has two different clear sky models: Ineichen and Haurwitz. We'll explore some of the features of each of them.
First, we need to make a Location
object so that pvlib
can calculate the solar position when needed.
from pvlib.location import Location
tus = Location(32.2, -111, 'US/Arizona', 700, 'Tucson')
print(tus)
Tucson: latitude=32.2, longitude=-111, tz=US/Arizona, altitude=700
times = pd.date_range(start=datetime.datetime(2014,1,1), end=datetime.datetime(2014,1,2), freq='1Min').tz_localize(tus.tz)
solpos = pvlib.solarposition.get_solarposition(times, tus, method='pyephem')
ephem_data = solpos
The Haurwitz model is a very simple model that only needs the solar zenith.
irrad_data = pvlib.clearsky.haurwitz(solpos['apparent_zenith'])
irrad_data.plot()
plt.ylabel('Irradiance (W/m^2)')
<matplotlib.text.Text at 0x7f582e23b198>
The ineichen
algorithm only requires you to supply the times and the location, but accepts many more optional parameters. It automatically calculates the solar position and looks up the Linke turbidity (related to the optical depth of the atmosphere).
irrad_data = pvlib.clearsky.ineichen(times, tus)
irrad_data.plot()
<matplotlib.axes._subplots.AxesSubplot at 0x7f582d853860>
The Linke turbidity lookup table uses monthly values, but these are interpolated down to daily values by default. You can also specify the value yourself. You can also supply the zenith angle to avoid recalculating the solar position each time the function is called.
solpos = pvlib.solarposition.get_solarposition(times, tus, method='pyephem')
fig, axes = plt.subplots(1,3, figsize=(16,5), sharey=True)
irrad_data = pvlib.clearsky.ineichen(times, tus, linke_turbidity=None, zenith_data=solpos['apparent_zenith'])
ax = axes[0]
irrad_data.plot(ax=ax)
ax.set_title('LT lookup table')
ax.set_ylabel('Irradiance W/m^2')
irrad_data = pvlib.clearsky.ineichen(times, tus, linke_turbidity=2.0, zenith_data=solpos['apparent_zenith'])
ax = axes[1]
irrad_data.plot(ax=ax)
ax.set_title('LT=2.0')
irrad_data35 = pvlib.clearsky.ineichen(times, tus, linke_turbidity=3.5, zenith_data=solpos['apparent_zenith'])
ax = axes[2]
irrad_data35.plot(ax=ax)
ax.set_title('LT=3.5')
<matplotlib.text.Text at 0x7f582e4a6470>
Here's a comparison between the clear sky algorithms.
ineichen_data = pvlib.clearsky.ineichen(times, tus, linke_turbidity=None, zenith_data=solpos['apparent_zenith'])
haurwitz_data = pvlib.clearsky.haurwitz(solpos['apparent_zenith'])
ineichen_data['GHI'].plot(label='Ineichen')
haurwitz_data['GHI'].plot(label='Haurwitz')
plt.ylabel('Irradiance W/m^2')
plt.legend()
<matplotlib.legend.Legend at 0x7f582d613780>
The grounddiffuse
function has a few different ways to obtain the diffuse light reflected from the ground given an surface tilt and the GHI.
First, you can specify the albedo of ground.
ground_irrad = pvlib.irradiance.grounddiffuse(40, irrad_data['GHI'], albedo=.25)
ground_irrad.plot()
plt.ylabel('Diffuse ground irradiance (W/m^2)')
<matplotlib.text.Text at 0x7f582d5dcf60>
Alternatively, you can specify the surface type with a string such as 'concrete'
or 'snow'
. All of the available surface_type
options are show in the plot below.
try:
sns.set_palette('husl', len(pvlib.irradiance.SURFACE_ALBEDOS.items()))
except:
pass
for surface, albedo in sorted(pvlib.irradiance.SURFACE_ALBEDOS.items(), key=lambda x: x[1], reverse=True):
ground_irrad = pvlib.irradiance.grounddiffuse(40, irrad_data['GHI'], surface_type=surface)
ground_irrad.plot(label='{}: {}'.format(surface, albedo))
plt.legend()
plt.ylabel('Diffuse ground irradiance (W/m^2)')
plt.title('Surface types')
<matplotlib.text.Text at 0x7f582d520358>
Next, vary the tilt angle. We expect to see maximum ground diffuse irradiance at a 90 deg tilt, and no ground diffuse irradiance at 0 tilt.
for surf_tilt in np.linspace(0, 90, 5):
ground_irrad = pvlib.irradiance.grounddiffuse(surf_tilt, irrad_data['GHI'])
ground_irrad.plot(label=surf_tilt)
plt.legend()
plt.ylabel('Diffuse ground irradiance (W/m^2)')
plt.title('Ground diffuse as a function of tilt')
<matplotlib.text.Text at 0x7f582d3ba320>
try:
sns.set_palette('deep')
except:
pass
pvlib
has many different ways to calculate the diffuse sky component of GHI.
The API for some of these functions needs some work.
The isotropic
model is the simplest model.
sky_diffuse = pvlib.irradiance.isotropic(40, irrad_data['DHI'])
sky_diffuse.plot(label='isotropic diffuse')
irrad_data['DHI'].plot()
irrad_data['GHI'].plot()
plt.legend()
plt.ylabel('Irradiance (W/m^2)')
<matplotlib.text.Text at 0x7f582c8fca90>
Compare just the POA diffuse to the input DHI.
sky_diffuse = pvlib.irradiance.isotropic(40, irrad_data['DHI'])
sky_diffuse.plot(label='isotropic diffuse')
irrad_data['DHI'].plot()
plt.legend()
plt.ylabel('Irradiance (W/m^2)')
<matplotlib.text.Text at 0x7f582c80ee10>
surf_tilt = 40
surf_az = 180
sky_diffuse = pvlib.irradiance.klucher(surf_tilt, surf_az, irrad_data['DHI'], irrad_data['GHI'],
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
sky_diffuse.plot(label='klucher diffuse')
irrad_data['DHI'].plot()
#irrad_data['GHI'].plot()
plt.legend()
plt.ylabel('Irradiance (W/m^2)')
<matplotlib.text.Text at 0x7f582c779c88>
surf_tilt = 40
surf_az = 180 # south facing
iso_diffuse = pvlib.irradiance.isotropic(surf_tilt, irrad_data['DHI'])
iso_diffuse.plot(label='isotropic diffuse')
klucher_diffuse = pvlib.irradiance.klucher(surf_tilt, surf_az, irrad_data['DHI'], irrad_data['GHI'],
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
klucher_diffuse.plot(label='klucher diffuse')
irrad_data['DHI'].plot()
plt.legend()
plt.ylabel('Irradiance (W/m^2)')
<matplotlib.text.Text at 0x7f582c6f6240>
Klucher as a function of surface azimuth.
surf_tilt = 40
irrad_data['DHI'].plot()
iso_diffuse = pvlib.irradiance.isotropic(surf_tilt, irrad_data['DHI'])
iso_diffuse.plot(label='isotropic')
for surf_az in np.linspace(0, 270, 4):
klucher_diffuse = pvlib.irradiance.klucher(surf_tilt, surf_az, irrad_data['DHI'], irrad_data['GHI'],
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
klucher_diffuse.plot(label='klucher: {}'.format(surf_az))
plt.legend()
<matplotlib.legend.Legend at 0x7f582c664c88>
Surface azimuth should not matter if tilt is 0.
surf_tilt = 0
irrad_data['DHI'].plot()
iso_diffuse = pvlib.irradiance.isotropic(surf_tilt, irrad_data['DHI'])
iso_diffuse.plot(label='isotropic')
for surf_az in np.linspace(0, 270, 4):
klucher_diffuse = pvlib.irradiance.klucher(surf_tilt, surf_az, irrad_data['DHI'], irrad_data['GHI'],
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
klucher_diffuse.plot(label='klucher: {}'.format(surf_az))
plt.legend()
<matplotlib.legend.Legend at 0x7f582d38ee10>
South facing at latitude.
surf_tilt = 32
surf_az = 180 # south facing
iso_diffuse = pvlib.irradiance.isotropic(surf_tilt, irrad_data['DHI'])
iso_diffuse.plot(label='isotropic diffuse')
klucher_diffuse = pvlib.irradiance.klucher(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['GHI'],
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
klucher_diffuse.plot(label='klucher diffuse')
dni_et = pvlib.irradiance.extraradiation(times.dayofyear)
reindl_diffuse = pvlib.irradiance.reindl(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['DNI'], irrad_data['GHI'], dni_et,
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
reindl_diffuse.plot(label='reindl diffuse')
irrad_data['DHI'].plot()
plt.legend()
<matplotlib.legend.Legend at 0x7f582c7398d0>
East facing
surf_tilt = 32
surf_az = 90
iso_diffuse = pvlib.irradiance.isotropic(surf_tilt, irrad_data['DHI'])
iso_diffuse.plot(label='isotropic diffuse')
klucher_diffuse = pvlib.irradiance.klucher(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['GHI'],
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
klucher_diffuse.plot(label='klucher diffuse')
dni_et = pvlib.irradiance.extraradiation(times.dayofyear)
reindl_diffuse = pvlib.irradiance.reindl(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['DNI'], irrad_data['GHI'], dni_et,
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
reindl_diffuse.plot(label='reindl diffuse')
irrad_data['DHI'].plot()
plt.legend()
<matplotlib.legend.Legend at 0x7f582c812550>
Hay-Davies facing south.
surf_tilt = 32
surf_az = 180
iso_diffuse = pvlib.irradiance.isotropic(surf_tilt, irrad_data['DHI'])
iso_diffuse.plot(label='isotropic diffuse')
klucher_diffuse = pvlib.irradiance.klucher(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['GHI'],
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
klucher_diffuse.plot(label='klucher diffuse')
dni_et = pvlib.irradiance.extraradiation(times.dayofyear)
haydavies_diffuse = pvlib.irradiance.haydavies(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['DNI'], dni_et,
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
haydavies_diffuse.plot(label='haydavies diffuse')
reindl_diffuse = pvlib.irradiance.reindl(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['DNI'], irrad_data['GHI'], dni_et,
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
reindl_diffuse.plot(label='reindl diffuse')
irrad_data['DHI'].plot()
plt.legend()
<matplotlib.legend.Legend at 0x7f582c4324e0>
Facing east.
surf_tilt = 32
surf_az = 90
iso_diffuse = pvlib.irradiance.isotropic(surf_tilt, irrad_data['DHI'])
iso_diffuse.plot(label='isotropic diffuse')
klucher_diffuse = pvlib.irradiance.klucher(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['GHI'],
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
klucher_diffuse.plot(label='klucher diffuse')
dni_et = pvlib.irradiance.extraradiation(times.dayofyear)
haydavies_diffuse = pvlib.irradiance.haydavies(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['DNI'], dni_et,
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
haydavies_diffuse.plot(label='haydavies diffuse')
reindl_diffuse = pvlib.irradiance.reindl(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['DNI'], irrad_data['GHI'], dni_et,
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
reindl_diffuse.plot(label='reindl diffuse')
irrad_data['DHI'].plot()
plt.legend()
<matplotlib.legend.Legend at 0x7f582c419da0>
Hay-Davies appears to be very similar to Reindl. Too similar?
surf_tilt = 32
surf_az = 90
iso_diffuse = pvlib.irradiance.isotropic(surf_tilt, irrad_data['DHI'])
iso_diffuse.plot(label='isotropic diffuse')
klucher_diffuse = pvlib.irradiance.klucher(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['GHI'],
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
klucher_diffuse.plot(label='klucher diffuse')
dni_et = pvlib.irradiance.extraradiation(times.dayofyear)
haydavies_diffuse = pvlib.irradiance.haydavies(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['DNI'], dni_et,
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
haydavies_diffuse.plot(label='haydavies diffuse')
king_diffuse = pvlib.irradiance.king(surf_tilt,irrad_data['DHI'], irrad_data['GHI'], ephem_data['apparent_zenith'])
king_diffuse.plot(label='king diffuse')
irrad_data['DHI'].plot()
plt.legend()
<matplotlib.legend.Legend at 0x7f582c17dbe0>
This section walks through the Perez algorithm.
sun_zen = ephem_data['apparent_zenith']
sun_az = ephem_data['apparent_azimuth']
DNI = irrad_data['DNI']
DHI = irrad_data['DHI']
DNI_ET = pvlib.irradiance.extraradiation(times.dayofyear)
AM = pvlib.atmosphere.relativeairmass(sun_zen)
surf_tilt = 32
surf_az = 180
kappa = 1.041 #for sun_zen in radians
z = np.radians(sun_zen) # convert to radians
#Dhfilter = DHI > 0
# epsilon is the sky's clearness
eps = ( (DHI + DNI)/DHI + kappa*(z**3) ) / ( 1 + kappa*(z**3) )
eps.plot()
<matplotlib.axes._subplots.AxesSubplot at 0x7f582c19a668>
ebin = eps.copy()
ebin[(eps<1.065)] = 1
ebin[(eps>=1.065) & (eps<1.23)] = 2
ebin[(eps>=1.23) & (eps<1.5)] = 3
ebin[(eps>=1.5) & (eps<1.95)] = 4
ebin[(eps>=1.95) & (eps<2.8)] = 5
ebin[(eps>=2.8) & (eps<4.5)] = 6
ebin[(eps>=4.5) & (eps<6.2)] = 7
ebin[eps>=6.2] = 8
ebin.plot()
plt.ylim(0,9)
(0, 9)
ebin = ebin - 1
ebin = ebin.dropna().astype(int)
ebin.plot()
<matplotlib.axes._subplots.AxesSubplot at 0x7f5826b85748>
delta = DHI * AM / DNI_ET
delta = delta[ebin.index]
delta.plot()
<matplotlib.axes._subplots.AxesSubplot at 0x7f5826be87f0>
z = z[ebin.index]
modelt = 'allsitescomposite1990'
F1c, F2c = pvlib.irradiance._get_perez_coefficients(modelt)
F1 = F1c[ebin,0] + F1c[ebin,1]*delta + F1c[ebin,2]*z
F1[F1<0]=0;
F1=F1.astype(float)
#F2= F2c[ebin,0] + F2c[ebin,1]*delta[ebinfilter] + F2c[ebin,2]*z[ebinfilter]
F2= F2c[ebin,0] + F2c[ebin,1]*delta + F2c[ebin,2]*z
F2[F2<0]=0
F2=F2.astype(float)
F1.plot(label='F1')
F2.plot(label='F2')
plt.legend()
<matplotlib.legend.Legend at 0x7f5826a0a8d0>
from pvlib import tools
A = tools.cosd(surf_tilt)*tools.cosd(sun_zen) + tools.sind(surf_tilt)*tools.sind(sun_zen)*tools.cosd(sun_az-surf_az) #removed +180 from azimuth modifier: Rob Andrews October 19th 2012
#A[A < 0] = 0
B = tools.cosd(sun_zen);
#B[B < pvl_tools.cosd(85)] = pvl_tools.cosd(85)
A.plot(label='A')
B.plot(label='B')
plt.legend()
<matplotlib.legend.Legend at 0x7f58269cd320>
sky_diffuse = DHI[ebin.index]*( 0.5* (1-F1)*(1+tools.cosd(surf_tilt))+F1 * A[ebin.index]/ B[ebin.index] + F2*tools.sind(surf_tilt))
sky_diffuse[sky_diffuse < 0] = 0
sky_diffuse.plot()
<matplotlib.axes._subplots.AxesSubplot at 0x7f582696fc50>
Compare the Perez model to others.
sun_zen = ephem_data['apparent_zenith']
sun_az = ephem_data['apparent_azimuth']
DNI = irrad_data['DNI']
DHI = irrad_data['DHI']
DNI_ET = pvlib.irradiance.extraradiation(times.dayofyear)
AM = pvlib.atmosphere.relativeairmass(sun_zen)
surf_tilt = 32
surf_az = 180
iso_diffuse = pvlib.irradiance.isotropic(surf_tilt, irrad_data['DHI'])
iso_diffuse.plot(label='isotropic diffuse')
klucher_diffuse = pvlib.irradiance.klucher(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['GHI'],
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
klucher_diffuse.plot(label='klucher diffuse')
dni_et = pvlib.irradiance.extraradiation(times.dayofyear)
haydavies_diffuse = pvlib.irradiance.haydavies(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['DNI'], dni_et,
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
haydavies_diffuse.plot(label='haydavies diffuse')
perez_diffuse = pvlib.irradiance.perez(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['DNI'], dni_et,
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'],
AM)
perez_diffuse.plot(label='perez diffuse')
irrad_data['DHI'].plot()
plt.legend()
<matplotlib.legend.Legend at 0x7f58267c2b38>
sun_zen = ephem_data['apparent_zenith']
sun_az = ephem_data['apparent_azimuth']
DNI = irrad_data['DNI']
DHI = irrad_data['DHI']
DNI_ET = pvlib.irradiance.extraradiation(times.dayofyear)
AM = pvlib.atmosphere.relativeairmass(sun_zen)
surf_tilt = 32
surf_az = 90
iso_diffuse = pvlib.irradiance.isotropic(surf_tilt, irrad_data['DHI'])
iso_diffuse.plot(label='isotropic diffuse')
klucher_diffuse = pvlib.irradiance.klucher(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['GHI'],
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
klucher_diffuse.plot(label='klucher diffuse')
dni_et = pvlib.irradiance.extraradiation(times.dayofyear)
haydavies_diffuse = pvlib.irradiance.haydavies(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['DNI'], dni_et,
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
haydavies_diffuse.plot(label='haydavies diffuse')
perez_diffuse = pvlib.irradiance.perez(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['DNI'], dni_et,
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'],
AM)
perez_diffuse.plot(label='perez diffuse')
irrad_data['DHI'].plot()
plt.legend()
<matplotlib.legend.Legend at 0x7f58268960f0>
Examine the impact of the coeffecient selection.
perez_diffuse = pvlib.irradiance.perez(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['DNI'], dni_et,
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'],
AM, modelt='allsitescomposite1990')
perez_diffuse.plot(label='allsitescomposite1990')
perez_diffuse = pvlib.irradiance.perez(surf_tilt, surf_az,
irrad_data['DHI'], irrad_data['DNI'], dni_et,
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'],
AM, modelt='phoenix1988')
perez_diffuse.plot(label='phoenix1988')
plt.legend()
<matplotlib.legend.Legend at 0x7f5826620240>
The irradiance
module has some convenience functions to help calculate the angle of incidence.
First, the angle of incidence.
proj = pvlib.irradiance.aoi(32, 180, ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
proj.plot()
#plt.ylim(-1.1,1.1)
plt.legend()
<matplotlib.legend.Legend at 0x7f582687c780>
AOI projection: the dot production of the surface normal and the vector to the sun.
proj = pvlib.irradiance.aoi_projection(32, 180, ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
proj.plot()
plt.ylim(-1.1,1.1)
plt.legend()
<matplotlib.legend.Legend at 0x7f5826758438>
The ratio between POA projection and the horizontal projection.
ratio = pvlib.irradiance.poa_horizontal_ratio(32, 180, ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
ratio.plot()
plt.ylim(-4,4)
(-4, 4)
This plot shows that an explicit dot product calculation gives the same result as aoi_projection
.
surf_tilt = 90
surf_az = 90
sen_alt_rad = np.radians(90 - surf_tilt)
sen_azi_rad = np.radians(surf_az)
alts = np.radians(90 - ephem_data['apparent_zenith'])
azis = np.radians(ephem_data['apparent_azimuth'])
dotprod = np.cos(sen_alt_rad)*np.cos(alts)*np.cos(sen_azi_rad-azis) + np.sin(sen_alt_rad)*np.sin(alts)
dotprod.plot(label='dotprod')
proj = pvlib.irradiance.aoi_projection(surf_tilt, surf_az, ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'])
proj.plot()
plt.ylim(-1.1,1.1)
plt.legend()
<matplotlib.legend.Legend at 0x7f58265454a8>
There is an experimental convenience function total_irrad
that aims to make it easier to play with different models. For now, we use it to make summary plots of the models explored above.
South facing with latitude tilt.
models = ['isotropic', 'klutcher', 'haydavies', 'reindl', 'king', 'perez']
totals = {}
for model in models:
total = pvlib.irradiance.total_irrad(32, 180,
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'],
DNI=irrad_data['DNI'], GHI=irrad_data['GHI'], DHI=irrad_data['DHI'],
DNI_ET=dni_et, AM=AM,
model=model,
surface_type='urban')
totals[model] = total
total.plot()
plt.title(model)
plt.ylabel('Irradiance (W/m^2)')
plt.figure()
for model, total in totals.items():
total['total'].plot(lw=.5, label=model)
plt.legend()
plt.ylabel('Irradiance (W/m^2)')
<matplotlib.text.Text at 0x7f5826127668>
tilt = 0
models = ['isotropic', 'klutcher', 'haydavies', 'reindl', 'king', 'perez']
totals = {}
for model in models:
total = pvlib.irradiance.total_irrad(0, 180,
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'],
DNI=irrad_data['DNI'], GHI=irrad_data['GHI'], DHI=irrad_data['DHI'],
DNI_ET=dni_et, AM=AM,
model=model,
surface_type='urban')
totals[model] = total
total.plot()
plt.title(model)
plt.ylabel('Irradiance (W/m^2)')
plt.figure()
for model, total in totals.items():
total['total'].plot(lw=.5, label=model)
plt.legend()
plt.ylabel('Irradiance (W/m^2)')
<matplotlib.text.Text at 0x7f582638df60>
East facing with latitude tilt.
models = ['isotropic', 'klutcher', 'haydavies', 'reindl', 'king', 'perez']
totals = {}
for model in models:
total = pvlib.irradiance.total_irrad(32, 90,
ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'],
DNI=irrad_data['DNI'], GHI=irrad_data['GHI'], DHI=irrad_data['DHI'],
DNI_ET=dni_et, AM=AM,
model=model,
surface_type='urban')
totals[model] = total
total.plot()
plt.title(model)
plt.ylabel('Irradiance (W/m^2)')
plt.figure()
for model, total in totals.items():
total['total'].plot(lw=.5, label=model)
plt.legend()
plt.ylabel('Irradiance (W/m^2)')
<matplotlib.text.Text at 0x7f5825c3ad30>