Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Add option to control airmass/perez enhancement factor in clearsky.ineichen #459

Merged
merged 18 commits into from
Aug 6, 2018
Merged

Add option to control airmass/perez enhancement factor in clearsky.ineichen #459

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
18 commits
Select commit Hold shift + click to select a range
8ead0c9
add perez_enhancement option
wholmgren May 14, 2018
de31833
pass kwargs through location.get_clearsky to ineichen
wholmgren May 14, 2018
b06a309
Merge branch 'master' into ineichenfix
wholmgren Jun 16, 2018
bba8dc0
Merge branch 'master' into ineichenfix
wholmgren Jun 25, 2018
313aaf9
hard code test values to avoid test failures
wholmgren Jun 25, 2018
517efcc
refactor test_irradiance.py input data into fixtures
wholmgren Jun 25, 2018
2147813
refactor location tests to avoid tight cs dependence
wholmgren Jun 25, 2018
7d64c5d
mock/refactor all tests except losses
wholmgren Jun 26, 2018
2fc2979
Merge remote-tracking branch 'pvlib/master' into ineichenfix
wholmgren Jun 26, 2018
01dcc55
Merge branch 'master' into ineichenfix
wholmgren Aug 1, 2018
91964ab
fix typo in test
wholmgren Aug 1, 2018
bd697bb
update whatsnew
wholmgren Aug 1, 2018
40f3385
update test ineichen values for perez_enhancement=False
wholmgren Aug 1, 2018
ec29664
update test_clearsky values
wholmgren Aug 1, 2018
dcc1224
add test for perez enhancement
wholmgren Aug 1, 2018
bfcbb13
update tests for py2.7-3.5
wholmgren Aug 2, 2018
84f344f
add requires_scipy for min env test
wholmgren Aug 2, 2018
c750fff
update comment to point to correct refs
wholmgren Aug 2, 2018
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
13 changes: 11 additions & 2 deletions docs/sphinx/source/whatsnew/v0.6.0.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,12 @@ API Changes
support from PVSystem.pvwatts_losses. Enables custom losses specification
in ModelChain calculations. (:issue:`484`)
* removed irradiance parameter from ModelChain.run_model and ModelChain.prepare_inputs
* Add ``perez_enhancement`` keyword argument to clearsky.ineichen to control
whether or not the "perez enhancement factor" is applied. The enhancement
factor was always applied until now. Now it is turned off by default. The
enhancement factor can yield unphysical results, especially for latitudes
closer to the poles and especially in the winter months. It may yield
improved results under other conditions. (:issue:`435`)


Enhancements
Expand Down Expand Up @@ -81,8 +87,11 @@ Documentation
Testing
~~~~~~~
* Add pytest-mock dependency
* Use pytest-mock to ensure that PVSystem methods call corresponding functions
correctly. Removes implicit dependence on precise return values of functions
* Use pytest-mock to ensure that PVSystem and ModelChain methods call
corresponding functions correctly. Removes implicit dependence on precise
return values of some function/methods. (:issue:`394`)
* Additional test refactoring to limit test result dependence to a single
function per test. (:issue:`394`)
* Use pytest-mock to ensure that ModelChain DC model is set up correctly.


Expand Down
41 changes: 16 additions & 25 deletions pvlib/clearsky.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@


def ineichen(apparent_zenith, airmass_absolute, linke_turbidity,
altitude=0, dni_extra=1364.):
altitude=0, dni_extra=1364., perez_enhancement=False):
'''
Determine clear sky GHI, DNI, and DHI from Ineichen/Perez model.

Expand Down Expand Up @@ -47,6 +47,12 @@ def ineichen(apparent_zenith, airmass_absolute, linke_turbidity,
Extraterrestrial irradiance. The units of ``dni_extra``
determine the units of the output.

perez_enhancement : bool, default False
Controls if the Perez enhancement factor should be applied.
Setting to True may produce spurious results for times when
the Sun is near the horizon and the airmass is high.
See https://github.com/pvlib/pvlib-python/issues/435

Returns
-------
clearsky : DataFrame (if Series input) or OrderedDict of arrays
Expand Down Expand Up @@ -79,28 +85,9 @@ def ineichen(apparent_zenith, airmass_absolute, linke_turbidity,
ISES Solar World Congress, June 2003. Goteborg, Sweden.
'''

# 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)).
# ghi is calculated using either the equations in [1] by setting
# perez_enhancement=False (default behavior) or using the model
# in [2] by setting perez_enhancement=True.

# The NaN handling is a little subtle. The AM input is likely to
# have NaNs that we'll want to map to 0s in the output. However, we
Expand All @@ -119,8 +106,12 @@ def ineichen(apparent_zenith, airmass_absolute, linke_turbidity,
cg1 = 5.09e-05 * altitude + 0.868
cg2 = 3.92e-05 * altitude + 0.0387

ghi = (np.exp(-cg2*airmass_absolute*(fh1 + fh2*(tl - 1))) *
np.exp(0.01*airmass_absolute**1.8))
ghi = np.exp(-cg2*airmass_absolute*(fh1 + fh2*(tl - 1)))

# https://github.com/pvlib/pvlib-python/issues/435
if perez_enhancement:
ghi *= np.exp(0.01*airmass_absolute**1.8)

# use fmax to map airmass nans to 0s. multiply and divide by tl to
# reinsert tl nans
ghi = cg1 * dni_extra * cos_zenith * tl / tl * np.fmax(ghi, 0)
Expand Down
2 changes: 1 addition & 1 deletion pvlib/location.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -218,7 +218,7 @@ def get_clearsky(self, times, model='ineichen', solar_position=None,

cs = clearsky.ineichen(apparent_zenith, airmass_absolute,
linke_turbidity, altitude=self.altitude,
dni_extra=dni_extra)
dni_extra=dni_extra, **kwargs)
elif model == 'haurwitz':
cs = clearsky.haurwitz(apparent_zenith)
elif model == 'simplified_solis':
Expand Down
144 changes: 88 additions & 56 deletions pvlib/test/test_clearsky.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
from collections import OrderedDict

import numpy as np
from numpy import nan
import pandas as pd
import pytz

Expand All @@ -20,35 +21,66 @@


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))
times = pd.date_range(start='2014-06-24', end='2014-06-25', freq='3h',
tz='America/Phoenix')
apparent_zenith = pd.Series(np.array(
[124.0390863, 113.38779941, 82.85457044, 46.0467599, 10.56413562,
34.86074109, 72.41687122, 105.69538659, 124.05614124]),
index=times)
am = pd.Series(np.array(
[nan, nan, 6.97935524, 1.32355476, 0.93527685,
cwhanse marked this conversation as resolved.
Show resolved Hide resolved
1.12008114, 3.01614096, nan, nan]),
index=times)
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. ]]),
array([[ 0. , 0. , 0. ],
[ 0. , 0. , 0. ],
[ 65.49426624, 321.16092181, 25.54562017],
[ 704.6968125 , 888.90147035, 87.73601277],
[1044.1230677 , 953.24925854, 107.03109696],
[ 853.02065704, 922.06124712, 96.42909484],
[ 251.99427693, 655.44925241, 53.9901349 ],
[ 0. , 0. , 0. ],
[ 0. , 0. , 0. ]]),
columns=['ghi', 'dni', 'dhi'],
index=times_localized)
index=times)

out = clearsky.ineichen(ephem_data['apparent_zenith'], am, 3)
out = clearsky.ineichen(apparent_zenith, am, 3)
assert_frame_equal(expected, out)


def test_ineichen_series_perez_enhancement():
times = pd.date_range(start='2014-06-24', end='2014-06-25', freq='3h',
tz='America/Phoenix')
apparent_zenith = pd.Series(np.array(
[124.0390863, 113.38779941, 82.85457044, 46.0467599, 10.56413562,
34.86074109, 72.41687122, 105.69538659, 124.05614124]),
index=times)
am = pd.Series(np.array(
[nan, nan, 6.97935524, 1.32355476, 0.93527685,
1.12008114, 3.01614096, nan, nan]),
index=times)
expected = pd.DataFrame(np.
array([[ 0. , 0. , 0. ],
[ 0. , 0. , 0. ],
[ 91.1249279 , 321.16092171, 51.17628184],
[ 716.46580547, 888.9014706 , 99.50500553],
[1053.42066073, 953.24925905, 116.3286895 ],
[ 863.54692748, 922.06124652, 106.9553658 ],
[ 271.06382275, 655.44925213, 73.05968076],
[ 0. , 0. , 0. ],
[ 0. , 0. , 0. ]]),
columns=['ghi', 'dni', 'dhi'],
index=times)

out = clearsky.ineichen(apparent_zenith, am, 3, perez_enhancement=True)
assert_frame_equal(expected, out)


def test_ineichen_scalar_input():
expected = OrderedDict()
expected['ghi'] = 1048.592893113678
expected['ghi'] = 1038.159219
expected['dni'] = 942.2081860378344
expected['dhi'] = 120.6989665520498
expected['dhi'] = 110.26529293612793

out = clearsky.ineichen(10., 1., 3.)
for k, v in expected.items():
Expand All @@ -74,9 +106,9 @@ def test_ineichen_nans():
expected['dni'] = np.full(length, np.nan)
expected['dhi'] = np.full(length, np.nan)

expected['ghi'][length-1] = 1053.205472
expected['dni'][length-1] = 946.352797
expected['dhi'][length-1] = 121.2299
expected['ghi'][length-1] = 1042.72590228
expected['dni'][length-1] = 946.35279683
expected['dhi'][length-1] = 110.75033088

out = clearsky.ineichen(apparent_zenith, airmass_absolute,
linke_turbidity, dni_extra=dni_extra)
Expand All @@ -89,43 +121,43 @@ def test_ineichen_arrays():
expected = OrderedDict()

expected['ghi'] = (np.
array([[[ 1106.78342709, 1064.7691287 , 1024.34972343],
[ 847.84529406, 815.66047425, 784.69741345],
[ 192.19092519, 184.89521884, 177.87646277]],
array([[[1095.77074798, 1054.17449885, 1014.15727338],
[ 839.40909243, 807.54451692, 776.88954373],
[ 190.27859353, 183.05548067, 176.10656239]],

[[ 959.12310134, 775.2374976 , 626.60692548],
[ 734.73092205, 593.86637713, 480.00875328],
[ 166.54997871, 134.61857872, 108.80915072]],
[[ 773.49041181, 625.19479557, 505.33080493],
[ 592.52803177, 478.92699901, 387.10585505],
[ 134.31520045, 108.56393694, 87.74977339]],

[[ 1026.15144142, 696.85030591, 473.22483724],
[ 786.0776095 , 533.81830453, 362.51125692],
[ 178.18932781, 121.00678573, 82.17463061]]]))
[[ 545.9968869 , 370.78162375, 251.79449885],
[ 418.25788117, 284.03520249, 192.88577665],
[ 94.81136442, 64.38555328, 43.72365587]]]))

expected['dni'] = (np.
array([[[ 1024.58284359, 942.20818604, 861.11344424],
[ 1024.58284359, 942.20818604, 861.11344424],
[ 1024.58284359, 942.20818604, 861.11344424]],
array([[[1014.38807396, 942.20818604, 861.11344424],
[1014.38807396, 942.20818604, 861.11344424],
[1014.38807396, 942.20818604, 861.11344424]],

[[ 687.61305142, 419.14891162, 255.50098235],
[ 687.61305142, 419.14891162, 255.50098235],
[ 687.61305142, 419.14891162, 255.50098235]],
[[ 687.61305142, 419.14891162, 255.50098235],
[ 687.61305142, 419.14891162, 255.50098235],
[ 687.61305142, 419.14891162, 255.50098235]],

[[ 458.62196014, 186.46177428, 75.80970012],
[ 458.62196014, 186.46177428, 75.80970012],
[ 458.62196014, 186.46177428, 75.80970012]]]))
[[ 458.62196014, 186.46177428, 75.80970012],
[ 458.62196014, 186.46177428, 75.80970012],
[ 458.62196014, 186.46177428, 75.80970012]]]))

expected['dhi'] = (np.
array([[[ 82.20058349, 122.56094266, 163.23627919],
[ 62.96930021, 93.88712907, 125.04624459],
[ 14.27398153, 21.28248435, 28.34568241]],
array([[[ 81.38267402, 111.96631281, 153.04382915],
[ 62.3427452 , 85.77117175, 117.23837487],
[ 14.13195304, 19.44274618, 26.57578203]],

[[ 271.51004993, 356.08858598, 371.10594313],
[ 207.988765 , 272.77968255, 284.28364554],
[ 47.14722539, 61.83413404, 64.44187075]],
[[ 85.87736039, 206.04588395, 249.82982258],
[ 65.78587472, 157.84030442, 191.38074731],
[ 14.91244713, 35.77949226, 43.38249342]],

[[ 567.52948128, 510.38853163, 397.41513712],
[ 434.75280544, 390.98029849, 304.4376574 ],
[ 98.5504602 , 88.62803842, 69.01041434]]]))
[[ 87.37492676, 184.31984947, 175.98479873],
[ 66.93307711, 141.19719644, 134.81217714],
[ 15.17249681, 32.00680597, 30.5594396 ]]]))

apparent_zenith = np.linspace(0, 80, 3)
airmass_absolute = np.linspace(1, 10, 3)
Expand All @@ -142,7 +174,7 @@ def test_ineichen_arrays():

def test_ineichen_dni_extra():
expected = pd.DataFrame(
np.array([[ 1053.20547182, 946.35279683, 121.22990042]]),
np.array([[1042.72590228, 946.35279683, 110.75033088]]),
columns=['ghi', 'dni', 'dhi'])

out = clearsky.ineichen(10, 1, 3, dni_extra=pd.Series(1370))
Expand All @@ -151,7 +183,7 @@ def test_ineichen_dni_extra():

def test_ineichen_altitude():
expected = pd.DataFrame(
np.array([[ 1145.64245696, 994.95377835, 165.80426215]]),
np.array([[1134.24312405, 994.95377835, 154.40492924]]),
columns=['ghi', 'dni', 'dhi'])

out = clearsky.ineichen(10, 1, 3, altitude=pd.Series(2000))
Expand Down Expand Up @@ -526,18 +558,18 @@ def test_detect_clearsky_components(detect_clearsky_data):
assert_series_equal(expected['Clear or not'], clear_samples,
check_dtype=False, check_names=False)
assert isinstance(components, OrderedDict)
assert np.allclose(alpha, 0.95345573579557108)
assert np.allclose(alpha, 0.9633903181941296)


@requires_scipy
def test_detect_clearsky_iterations(detect_clearsky_data):
expected, cs = detect_clearsky_data
alpha = 1.0348
alpha = 1.0448
with pytest.warns(RuntimeWarning):
clear_samples = clearsky.detect_clearsky(
expected['GHI'], cs['ghi']*alpha, cs.index, 10, max_iterations=1)
assert (clear_samples[:'2012-04-01 10:39:00'] == True).all()
assert (clear_samples['2012-04-01 10:40:00':] == False).all()
assert (clear_samples[:'2012-04-01 10:41:00'] == True).all()
assert (clear_samples['2012-04-01 10:42:00':] == False).all()
clear_samples = clearsky.detect_clearsky(
expected['GHI'], cs['ghi']*alpha, cs.index, 10, max_iterations=20)
assert_series_equal(expected['Clear or not'], clear_samples,
Expand Down