Clifford is a distinguished member of the Technical Staff at Sandia National Laboratories. He is working on methods for modeling of PV system performance, including quantification of uncertainty in model predictions, improvements to models, and the estimation of model coefficients from measurements. He holds degrees in Mathematics from The George Washington University, Northwestern University and Brigham Young University.
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Accuracy of performance prediction for PV systems
Predicting power and energy production from PV systems entails use of a sequence of empirical models. For illustration, a modeler typically begins with meteorological data and a system design, then estimates plane of array irradiance over the PV system’s footprint, temperature of the PV modules, DC power production and inverter conversion efficiencies, while taking into account module soiling, spectral variations in irradiance, and variation of electrical characteristics among modules. Error in the end result is thus an aggregation of errors in data and models that comprise the calculation. What accuracy can be expected for the prediction of power and energy is one question. We will briefly survey the modeling process and the required data and will summarize recent analyses and research that address prediction accuracy.