Blind Photovoltaic Modeling Intercomparison: A Multidimensional Data Analysis and Lessons Learned

Marios Theristis; Nicholas Riedel-Lyngskaer; Joshua Stein; Lelia Deville; Leonardo Micheli; Anton Driesse; William Hobbs; Silvana Ovaitt; Rajiv Daxini; David Barrie; Mark Campanelli; Heather Hodges; Javier Ledesma; Ismael Lokhat; Brendan McCormick; Bin Meng; Bill Miller; Ricardo Motta; Emma Noirault; Megan Parker; Jesus Polo; Daniel Powell; Rodrigo Moreton; Matthew Prilliman; Steve Ransome; Martin Schneider; Branislav Schnierer; Bowen Tian; Frederick Warner; Robert Williams; Bruno Wittmer; Changrui Zhao

doi:10.1002/pip.3729

Blind Photovoltaic Modeling Intercomparison: A Multidimensional Data Analysis and Lessons Learned

Marios Theristis, Nicholas Riedel-Lyngskaer, Joshua Stein, Lelia Deville, Leonardo Micheli, Anton Driesse, William Hobbs, Silvana Ovaitt, Rajiv Daxini, David Barrie, Mark Campanelli, Heather Hodges, Javier Ledesma, Ismael Lokhat, Brendan McCormick, Bin Meng, Bill Miller, Ricardo Motta, Emma Noirault, Megan ParkerJesus Polo, Daniel Powell, Rodrigo Moreton, Matthew Prilliman, Steve Ransome, Martin Schneider, Branislav Schnierer, Bowen Tian, Frederick Warner, Robert Williams, Bruno Wittmer, Changrui Zhao

Research output: Contribution to journal › Article › peer-review

4 Scopus Citations

Abstract

The Photovoltaic (PV) Performance Modeling Collaborative (PVPMC) organized a blind PV performance modeling intercomparison to allow PV modelers to blindly test their models and modeling ability against real system data. Measured weather and irradiance data were provided along with detailed descriptions of PV systems from two locations (Albuquerque, New Mexico, USA, and Roskilde, Denmark). Participants were asked to simulate the plane-of-array irradiance, module temperature, and DC power output from six systems and submit their results to Sandia for processing. The results showed overall median mean bias (i.e., the average error per participant) of 0.6% in annual irradiation and -3.3% in annual energy yield. While most PV performance modeling results seem to exhibit higher precision and accuracy as compared to an earlier blind PV modeling study in 2010, human errors, modeling skills, and derates were found to still cause significant errors in the estimates.

Original language	American English
Pages (from-to)	1144-1157
Number of pages	14
Journal	Progress in Photovoltaics: Research and Applications
Volume	31
Issue number	11
DOIs	https://doi.org/10.1002/pip.3729
State	Published - 2023

NREL Publication Number

NREL/JA-5K00-87172

Keywords

blind comparison
modeling
performance
photovoltaic
yield model

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10.1002/pip.3729

https://www.nrel.gov/docs/fy24osti/87172.pdf

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Theristis, M., Riedel-Lyngskaer, N., Stein, J., Deville, L., Micheli, L., Driesse, A., Hobbs, W., Ovaitt, S., Daxini, R., Barrie, D., Campanelli, M., Hodges, H., Ledesma, J., Lokhat, I., McCormick, B., Meng, B., Miller, B., Motta, R., Noirault, E., ... Zhao, C. (2023). Blind Photovoltaic Modeling Intercomparison: A Multidimensional Data Analysis and Lessons Learned. Progress in Photovoltaics: Research and Applications, 31(11), 1144-1157. https://doi.org/10.1002/pip.3729

@article{22435ae0015c47e3af7380b37cbd778f,

title = "Blind Photovoltaic Modeling Intercomparison: A Multidimensional Data Analysis and Lessons Learned",

abstract = "The Photovoltaic (PV) Performance Modeling Collaborative (PVPMC) organized a blind PV performance modeling intercomparison to allow PV modelers to blindly test their models and modeling ability against real system data. Measured weather and irradiance data were provided along with detailed descriptions of PV systems from two locations (Albuquerque, New Mexico, USA, and Roskilde, Denmark). Participants were asked to simulate the plane-of-array irradiance, module temperature, and DC power output from six systems and submit their results to Sandia for processing. The results showed overall median mean bias (i.e., the average error per participant) of 0.6% in annual irradiation and -3.3% in annual energy yield. While most PV performance modeling results seem to exhibit higher precision and accuracy as compared to an earlier blind PV modeling study in 2010, human errors, modeling skills, and derates were found to still cause significant errors in the estimates.",

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author = "Marios Theristis and Nicholas Riedel-Lyngskaer and Joshua Stein and Lelia Deville and Leonardo Micheli and Anton Driesse and William Hobbs and Silvana Ovaitt and Rajiv Daxini and David Barrie and Mark Campanelli and Heather Hodges and Javier Ledesma and Ismael Lokhat and Brendan McCormick and Bin Meng and Bill Miller and Ricardo Motta and Emma Noirault and Megan Parker and Jesus Polo and Daniel Powell and Rodrigo Moreton and Matthew Prilliman and Steve Ransome and Martin Schneider and Branislav Schnierer and Bowen Tian and Frederick Warner and Robert Williams and Bruno Wittmer and Changrui Zhao",

year = "2023",

doi = "10.1002/pip.3729",

language = "American English",

volume = "31",

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journal = "Progress in Photovoltaics: Research and Applications",

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Theristis, M, Riedel-Lyngskaer, N, Stein, J, Deville, L, Micheli, L, Driesse, A, Hobbs, W, Ovaitt, S, Daxini, R, Barrie, D, Campanelli, M, Hodges, H, Ledesma, J, Lokhat, I, McCormick, B, Meng, B, Miller, B, Motta, R, Noirault, E, Parker, M, Polo, J, Powell, D, Moreton, R, Prilliman, M, Ransome, S, Schneider, M, Schnierer, B, Tian, B, Warner, F, Williams, R, Wittmer, B & Zhao, C 2023, 'Blind Photovoltaic Modeling Intercomparison: A Multidimensional Data Analysis and Lessons Learned', Progress in Photovoltaics: Research and Applications, vol. 31, no. 11, pp. 1144-1157. https://doi.org/10.1002/pip.3729

TY - JOUR

T1 - Blind Photovoltaic Modeling Intercomparison: A Multidimensional Data Analysis and Lessons Learned

AU - Theristis, Marios

AU - Riedel-Lyngskaer, Nicholas

AU - Stein, Joshua

AU - Deville, Lelia

AU - Micheli, Leonardo

AU - Driesse, Anton

AU - Hobbs, William

AU - Ovaitt, Silvana

AU - Daxini, Rajiv

AU - Barrie, David

AU - Campanelli, Mark

AU - Hodges, Heather

AU - Ledesma, Javier

AU - Lokhat, Ismael

AU - McCormick, Brendan

AU - Meng, Bin

AU - Miller, Bill

AU - Motta, Ricardo

AU - Noirault, Emma

AU - Parker, Megan

AU - Polo, Jesus

AU - Powell, Daniel

AU - Moreton, Rodrigo

AU - Prilliman, Matthew

AU - Ransome, Steve

AU - Schneider, Martin

AU - Schnierer, Branislav

AU - Tian, Bowen

AU - Warner, Frederick

AU - Williams, Robert

AU - Wittmer, Bruno

AU - Zhao, Changrui

PY - 2023

Y1 - 2023

N2 - The Photovoltaic (PV) Performance Modeling Collaborative (PVPMC) organized a blind PV performance modeling intercomparison to allow PV modelers to blindly test their models and modeling ability against real system data. Measured weather and irradiance data were provided along with detailed descriptions of PV systems from two locations (Albuquerque, New Mexico, USA, and Roskilde, Denmark). Participants were asked to simulate the plane-of-array irradiance, module temperature, and DC power output from six systems and submit their results to Sandia for processing. The results showed overall median mean bias (i.e., the average error per participant) of 0.6% in annual irradiation and -3.3% in annual energy yield. While most PV performance modeling results seem to exhibit higher precision and accuracy as compared to an earlier blind PV modeling study in 2010, human errors, modeling skills, and derates were found to still cause significant errors in the estimates.

AB - The Photovoltaic (PV) Performance Modeling Collaborative (PVPMC) organized a blind PV performance modeling intercomparison to allow PV modelers to blindly test their models and modeling ability against real system data. Measured weather and irradiance data were provided along with detailed descriptions of PV systems from two locations (Albuquerque, New Mexico, USA, and Roskilde, Denmark). Participants were asked to simulate the plane-of-array irradiance, module temperature, and DC power output from six systems and submit their results to Sandia for processing. The results showed overall median mean bias (i.e., the average error per participant) of 0.6% in annual irradiation and -3.3% in annual energy yield. While most PV performance modeling results seem to exhibit higher precision and accuracy as compared to an earlier blind PV modeling study in 2010, human errors, modeling skills, and derates were found to still cause significant errors in the estimates.

KW - blind comparison

KW - modeling

KW - performance

KW - photovoltaic

KW - yield model

UR - http://www.scopus.com/inward/record.url?scp=85165434886&partnerID=8YFLogxK

U2 - 10.1002/pip.3729

DO - 10.1002/pip.3729

M3 - Article

SN - 1062-7995

VL - 31

SP - 1144

EP - 1157

JO - Progress in Photovoltaics: Research and Applications

JF - Progress in Photovoltaics: Research and Applications

IS - 11

ER -

Blind Photovoltaic Modeling Intercomparison: A Multidimensional Data Analysis and Lessons Learned

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Keywords

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