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.

Open‐source photovoltaic model pipeline validation against well‐characterized system data

“…In such cases, a PV performance modeling comparison would be biased by uncertainties in the environmental and module specific characterization data rather than focusing on the ability of the models to predict a system's behavior. An international blind PV performance modeling comparison was recently published by the Sandia-led PV Performance Modeling Collaborative (PVPMC), involving participants from 32 institutions [7]. The results demonstrated improved precision among models, but accuracy still depends on the modeler's skill and derate assumptions.…”