Feature Review of Photovoltaic Modeling Software Utilizing Blind Performance Assessment: Article No. 114207

  • Lelia Deville
  • , Kevin Anderson
  • , Juergen Sutterlueti
  • , Terrence Chambers
  • , Karel De Brabandere
  • , Felix Cicala
  • , Javier Lopez-Lorente
  • , Brian Mirletz
  • , Anja Neubert
  • , Maitheli Nikam
  • , Michele Oliosi
  • , Matthew Prilliman
  • , Kurt Rhee
  • , Branislav Schnierer
  • , Jason Spokes
  • , Bruno Wittmer
  • , Marios Theristis

Research output: Contribution to journalArticlepeer-review

Abstract

While confidence in photovoltaic (PV) modeling software has always been essential, the rapid pace of new PV plant developments makes accuracy and credibility more critical than ever. Independent assessments, particularly through blind modeling comparisons, are therefore necessary to ensure unbiased benchmarking across PV modeling software. Previous studies have been limited by a narrow range of models compared, anonymized results, or system size. This study presents results from the first-ever onymous blind modeling comparison, evaluated using both lab- and utility-scale fixed-tilt, monofacial, south-facing systems at sub-hourly time intervals. Seven commercially used PV software tools were compared: 3E SynaptiQ, PlantPredict, PVsyst, RatedPower, SAM, SolarFarmer, and Solargis Evaluate. Predictions were submitted directly by software representatives, providing unique insights into each software's implementation and resulting prediction behavior. Notable features, including plane-of-array (POA) transposition model, module temperature model, shading model, and performance model were analyzed and compared. Four summary tables compile these features of the software, serving as a resource to help users understand the methodological differences and select the most suitable software for their applications. The software tools show deviations from mean error in annual yield up to 2.5 % in the lab-scale system, increasing to 6.0 % for the utility-scale system. These differences arise from a combination of user decisions and the inherent behavior of the software, indicating the need for continuous and rigorous validation of modeling methods using these software tools against complex, real-world systems.
Original languageAmerican English
Number of pages13
JournalSolar Energy
Volume304
DOIs
StatePublished - 2026

NLR Publication Number

  • NLR/JA-7A40-94745

Keywords

  • blind comparison
  • performance modeling
  • photovoltaic modeling
  • PV software

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