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Horizon coefficient should be allowed to go negative in the Perez transposition model (#1239)
* Merged PR 13463: Horizon coefficient should be allowed to go negative in the Perez transposition model Horizon coefficient should be allowed to go negative in the Perez transposition model Link to the original paper: https://www.osti.gov/servlets/purl/7024029 Section III.2 states this explicitly for the horizon component: "(2) The horizon brightening coefficient, F2, is negative for overcast and low E occurrences -- indicative of brightening of the zenithal region of the sky for these conditions. This becomes positive past intermediate conditions and increases substantially with clearness." Note that this change also impacts results from the DIRINT model (used to generate DNI from GHI). This change corresponds to revision 8 of the SAM vs PVLib comparison, found here: https://cleanpower1.sharepoint.com/sites/SolarAnywhere/Shared%20Documents/Power%20Model%20Discussions/PVLib_Comparison_Rev8_FixPvLibPerezModelF2Bound.zip * Merged PR 13472: Fix reported Stickler issues Fix reported Stickler issues This is an attempt to fix all formatting requirement failures reported by Stickler CI for PR 1239 to the pvlib-python project. Related work items: #18784 * Merged PR 13479: Final changes to meet formatting requirements I was shown how to validate this locally and fixed all issues, so this should be the last round. I couldn't cherry pick due to conflicts. but this shouldn't include any other changes. Related work items: #18784 * Add appropriate details of the bug fix to the 'what's new' content.
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‎docs/sphinx/source/whatsnew/v0.9.0.rst

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@@ -149,6 +149,12 @@ Enhancements
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Bug fixes
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~~~~~~~~~
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* Corrected an error in :py:func:`~pvlib.irradiance.perez` where the horizon
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irradiance component was prevented from taking negative values. Negative
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values are intentional according to the original publication. Changes in
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output are expected to be small and primarily occur at low irradiance
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conditions.
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(:issue:`1238`, :pull:`1239`)
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* Pass weather data to solar position calculations in
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:py:meth:`~pvlib.modelchain.ModelChain.prepare_inputs_from_poa`.
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(:issue:`1065`, :pull:`1140`)
@@ -192,3 +198,4 @@ Contributors
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* Tony Lorenzo (:ghuser:`alorenzo175`)
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* Damjan Postolovski (:ghuser:`dpostolovski`)
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* Joe Ranalli (:ghuser:`jranalli`)
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* Chas Schweizer (:ghuser:`cpr-chas`)

‎pvlib/irradiance.py

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@@ -1126,7 +1126,6 @@ def perez(surface_tilt, surface_azimuth, dhi, dni, dni_extra,
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F1 = np.maximum(F1, 0)
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F2 = (F2c[ebin, 0] + F2c[ebin, 1] * delta + F2c[ebin, 2] * z)
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F2 = np.maximum(F2, 0)
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A = aoi_projection(surface_tilt, surface_azimuth,
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solar_zenith, solar_azimuth)

‎pvlib/tests/test_irradiance.py

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@@ -248,6 +248,52 @@ def test_perez_components(irrad_data, ephem_data, dni_et, relative_airmass):
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assert_series_equal(sum_components, expected_for_sum, check_less_precise=2)
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def test_perez_negative_horizon():
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times = pd.date_range(start='20190101 11:30:00', freq='1H',
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periods=5, tz='US/Central')
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# Avoid test dependencies on functionality not being tested by hard-coding
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# the inputs. This data corresponds to Goodwin Creek in the afternoon on
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# 1/1/2019.
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# dni_e is slightly rounded from irradiance.get_extra_radiation
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# airmass from atmosphere.get_relative_airmas
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inputs = pd.DataFrame(np.array(
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[[ 158, 19, 1, 0, 0],
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[ 249, 165, 136, 93, 50],
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[ 57.746951, 57.564205, 60.813841, 66.989435, 75.353368],
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[ 171.003315, 187.346924, 202.974357, 216.725599, 228.317233],
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[1414, 1414, 1414, 1414, 1414],
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[ 1.869315, 1.859981, 2.044429, 2.544943, 3.900136]]).T,
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columns=['dni', 'dhi', 'solar_zenith',
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'solar_azimuth', 'dni_extra', 'airmass'],
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index=times
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)
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out = irradiance.perez(34, 180, inputs['dhi'], inputs['dni'],
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inputs['dni_extra'], inputs['solar_zenith'],
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inputs['solar_azimuth'], inputs['airmass'],
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model='allsitescomposite1990',
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return_components=True)
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# sky_diffuse can be less than isotropic under certain conditions as
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# horizon goes negative
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expected = pd.DataFrame(np.array(
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[[281.410185, 152.20879, 123.867898, 82.836412, 43.517015],
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[166.785419, 142.24475, 119.173875, 83.525150, 45.725931],
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[113.548755, 16.09757, 9.956174, 3.142467, 0],
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[ 1.076010, -6.13353, -5.262151, -3.831230, -2.208923]]).T,
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columns=['sky_diffuse', 'isotropic', 'circumsolar', 'horizon'],
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index=times
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)
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expected_for_sum = expected['sky_diffuse'].copy()
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sum_components = out.iloc[:, 1:].sum(axis=1)
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sum_components.name = 'sky_diffuse'
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assert_frame_equal(out, expected, check_less_precise=2)
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assert_series_equal(sum_components, expected_for_sum, check_less_precise=2)
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def test_perez_arrays(irrad_data, ephem_data, dni_et, relative_airmass):
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dni = irrad_data['dni'].copy()
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dni.iloc[2] = np.nan
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expected_col_order = ['ghi', 'dni', 'dhi']
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expected = pd.DataFrame(array(
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[[ 21.05796198, 0. , 21.05796198],
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[ 288.22574368, 60.59964218, 245.37532576],
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[ 291.40037163, 63.41290679, 246.56067523],
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[ 931.04078010, 695.94965324, 277.06172442]]),
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columns=expected_col_order, index=times)
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expected = pd.DataFrame(array(
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[[ 21.05796198, 0. , 21.05796198],
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[ 289.81109139, 60.52460392, 247.01373353],
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[ 293.21310935, 63.27500913, 248.47092131],
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[ 932.46756378, 648.05001357, 323.49974813]]),
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columns=expected_col_order, index=times)
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expected = pd.DataFrame(array(
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[[ 21.3592591, 0. , 21.3592591 ],
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[ 292.5162373, 64.42628826, 246.95997198],
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[ 941.6753031, 727.16311901, 258.36548605]]),
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[ 294.4985420, 66.25848451, 247.64671830],
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[ 941.7943404, 727.50552952, 258.16276278]]),
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columns=expected_col_order, index=times)
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assert_frame_equal(output, expected)
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temp_dew=temp_dew)
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expected = pd.DataFrame(array(
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[[ 21.05796198, 0. , 21.05796198],
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[ 292.40468994, 36.79559287, 266.3862767 ],
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[[ 21.05796198, 0., 21.05796198],
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[ 295.06070190, 38.20346345, 268.0467738],
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[ 931.79627208, 689.81549269, 283.5817439]]),
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columns=expected_col_order, index=times)
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