spectrum.rst

.. currentmodule:: pvlib.spectrum

Spectrum

The spectrum functionality of pvlib-python includes simulating clear sky spectral irradiance curves, calculating the spectral mismatch factor for a range of single-junction PV cell technologies, and other calculations such as converting between spectral response and EQE, and computing average photon energy values from spectral irradiance data.

This user guide page summarizes some of pvlib-python's spectrum-related capabilities, starting with a summary of spectral mismatch estimation models available in pvlib-python.

Spectral mismatch models

The spectral mismatch factor is the ratio of a PV device's response under a given spectrum to its response under a reference spectrum, typically the AM1.5G spectrum. It represents the relative difference in the performance of a PV device under a spectrum different from the reference spectrum, and can be used to correct the measured power output of a PV system for spectral effects.

pvlib-python contains several models to estimate the spectral mismatch factor using atmospheric variables such as air mass, or calculate it exactly using system and meteorological data such as spectral response and spectral irradiance. Examples demonstrating the application of several spectral mismatch models using pvlib-python are also available: :ref:`sphx_glr_gallery_spectrum_spectral_factor.py` and Reference [1], the latter of which also contains downloadable spectral response and spectral irradiance data.

The table below summarizes the models currently available in pvlib, their required inputs, cell technologies for which model coefficients have been published, and references. Note that while most models are validated for specific cell technologies, the Sandia Array Performance Model (SAPM) is validated for a range of commercial modules. An extended review of a wider range of models available in the published literature may be found in Reference [2].

Model	Inputs	Default parameter availability						Reference
		mono-Si	poly-Si	CdTe	CIGS	a-Si	perovskite
:py:func:`Caballero <spectral_factor_caballero>`	:term:`airmass_absolute`,	✓	✓	✓	✓	✓	✓	[2]
	:term:`precipitable_water`,
	:term:`aod`
:py:func:`First Solar <spectral_factor_firstsolar>`	:term:`airmass_absolute`,		✓	✓				[3]
	:term:`precipitable_water`
:py:func:`JRC <spectral_factor_jrc>`	:term:`airmass_relative`,		✓	✓				[4]
	:term:`clearsky_index`
:py:func:`Polo <spectral_factor_polo>`	:term:`precipitable_water`,	✓		✓	✓	✓		[5]
	:term:`airmass_absolute`,
	:term:`aod500`
	:term:`aoi`,
	:term:`pressure`
:py:func:`PVSPEC <spectral_factor_pvspec>`	:term:`airmass_absolute`,	✓	✓	✓	✓	✓		[6]
	clearsky_index
:py:func:`SAPM <spectral_factor_sapm>`	:term:`airmass_absolute`							[7]

References

[1]

A. Driesse, J. S. Stein, and M. Theristis, "Global horizontal spectral irradiance and module spectral response measurements: an open dataset for PV research Sandia National Laboratories, ALbuquerque, NM, USA, Rep. SAND2023-02045, 2023. Available: https://datahub.duramat.org/dataset/module-sr-library

[2]	(1, 2) R. Daxini and Y. Wu, "Review of methods to account for the solar spectral influence on photovoltaic device performance," Energy, vol. 286, p. 129461, Jan. 2024. :doi:`10.1016/j.energy.2023.129461`

[3]	J. A. Caballero, E. Fernández, M. Theristis, F. Almonacid, and G. Nofuentes, "Spectral Corrections Based on Air Mass, Aerosol Optical Depth and Precipitable Water for PV Performance Modeling," IEEE Journal of Photovoltaics, vol. 8, no. 2, pp. 552–558, Mar. 2018. :doi:`10.1109/JPHOTOV.2017.2787019`

[4]	S. Pelland, J. Remund, and J. Kleissl, "Development and Testing of the PVSPEC Model of Photovoltaic Spectral Mismatch Factor," in Proc. 2020 IEEE 47th Photovoltaic Specialists Conference (PVSC), Calgary, AB, Canada, 2020, pp. 1–6. :doi:`10.1109/PVSC45281.2020.9300932`

[5]	J. Polo and C. Sanz-Saiz, 'Development of spectral mismatch models for BIPV applications in building façades', Renewable Energy, vol. 245, p. 122820, Jun. 2025, :doi:`10.1016/j.renene.2025.122820`

[6]	D. L. King, W. E. Boyson, and J. A. Kratochvil, Photovoltaic Array Performance Model, Sandia National Laboratories, Albuquerque, NM, USA, Tech. Rep. SAND2004-3535, Aug. 2004. :doi:`10.2172/919131`

[7]	M. Lee and A. Panchula, "Spectral Correction for Photovoltaic Module Performance Based on Air Mass and Precipitable Water," 2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC), Portland, OR, USA, 2016, pp. 3696-3699. :doi:`10.1109/PVSC.2016.7749836`

[8]	T. Huld, T. Sample, and E. Dunlop, "A Simple Model for Estimating the Influence of Spectrum Variations on PV Performance," pp. 3385–3389, Nov. 2009, :doi:`10.4229/24THEUPVSEC2009-4AV.3.27`

[9]	IEC 60904-7:2019, Photovoltaic devices — Part 7: Computation of the spectral mismatch correction for measurements of photovoltaic devices, International Electrotechnical Commission, Geneva, Switzerland, 2019.