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

Hyeunguk Ahn

Energy, 2024, vol. 290, issue C

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.

Keywords: Renewable energy; Bias correction; Forecasting accuracy; Physical method; Data validation; PV simulation (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:290:y:2024:i:c:s0360544223034904

DOI: 10.1016/j.energy.2023.130096

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