 What drives the accuracy of PV output forecasts?Thi Ngoc Nguyen and Felix Müsgens Applied Energy, 2022, vol. 323, issue C, No S0306261922009102 Abstract: In this paper, 180 papers on photovoltaic (PV) output forecasting were reviewed and a database of forecast errors was extracted for statistical analysis. The paper shows that among the forecast models, hybrid models are most likely to become the primary form of PV output forecasting in the future. The use of data processing techniques is positively correlated with the forecast quality, while the lengths of the forecast horizons and out-of-sample test sets have negative effects on the forecast accuracy. The paper also found that the use of data normalization, the wavelet transform, and the inclusion of clear sky index and numerical weather prediction variables are the most effective data processing techniques. Furthermore, the paper found some evidence of “cherry picking” in the reporting of errors and we recommend that the test sets be at least one year long to avoid any distortion in the performance of the models. Keywords: Solar energy forecasting; Photovoltaics forecasting; Evaluating forecasts; Forecasting practice; Model selection; Statistical analysis (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:323:y:2022:i:c:s0306261922009102 Ordering information: This journal article can be ordered from DOI: 10.1016/j.apenergy.2022.119603 Access Statistics for this article Applied Energy is currently edited by J. Yan More articles in Applied Energy from Elsevier |
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