Short-term PV power forecast methodology based on multi-scale fluctuation characteristics extraction

Jiebei Zhu, Mingrui Li, Lin Luo, Bidan Zhang, Mingjian Cui and Lujie Yu

Renewable Energy, 2023, vol. 208, issue C, 141-151

Abstract: The short-term forecast of photovoltaic (PV) power is crucial for the security and economics of power system operations. However, the fluctuation characteristics of the PV power, which are closely related to the meteorological factors, introduce inaccuracies in its forecast. Towards this end, the paper studies the effects of clustering analysis at long time scale and data reconstruction technique at short time scale on capturing PV power fluctuation characteristics. A short-term PV power forecasts method based on multi-scale fluctuation characteristics extraction (MFCE), which employs a path analysis to identify the relevance of meteorological factors with PV power at long time scale and a phase space reconstruction to analyze PV power fluctuation characteristics at short time scale, is proposed in this paper. The proposed MFCE methodology deploys a widely-used extreme gradient boosting (XGBoost) model to output the forecasting results. Both the effectiveness and accuracy of the proposed methodology are verified by using the real data under the conditions of sunny and cloudy days of four seasons compared to traditional methodologies.

Keywords: PV power generation; Fluctuation characteristics; Multi-scale analysis; Feature selection; Phase space reconstruction (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:208:y:2023:i:c:p:141-151

DOI: 10.1016/j.renene.2023.03.029

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