Blind photovoltaic modeling intercomparison: A multidimensional data analysis and lessons learned

Marios Theristis,Nicholas Riedel-Lyngskaer,Joshua S. S. Stein,Lelia Deville,Leonardo Micheli,Anton Driesse,William B. B. Hobbs,Silvana Ovaitt,Rajiv Daxini, David Barrie, Mark Campanelli, Heather Hodges,Javier R. R. 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

PROGRESS IN PHOTOVOLTAICS(2023)

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摘要
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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关键词
blind comparison, modeling, performance, photovoltaic, yield modeling
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