A framework for developing data-driven correction factors for solar PV systems

Abstract

Correcting simulated solar photovoltaic (PV) output poses challenges due to the limited availability of measured PV output data. This study introduces a framework for developing correction factors capable of adjusting bias errors in hourly simulated PV output across various levels of global horizontal irradiance (GHI). GHI-dependent correction factors are developed for each PV plant, with hourly simulated PV output validated against the measured output for 37 PV plants in South Korea. Performance evaluation using U95, a measure of model uncertainty, reveals a significant reduction (by up to 0.24) in prediction errors. The reduction is largely attributed to reductions of nMBE s (by up to 0.33) and partly to reductions of nRMSE s (by up to 0.11), demonstrating mitigation of both random and bias errors. The framework exhibits a promising reduction in forecasting errors for monthly energy yields and performance ratios. Given that the proposed framework requires a short length of training data (<4 months), its versatility allows for adoption by existing software packages relying on physical PV modeling, offering potential enhancements in forecasting accuracy for practical applications.