Short-term wind power interval prediction method using VMD-RFG and Att-GRU

Hongyi Liu, Hua Han, Yao Sun, Guangze Shi, Mei Su, Zhangjie Liu, Hongfei Wang and Xiaofei Deng

Energy, 2022, vol. 251, issue C

Abstract: With the increasing penetration of wind energy, accurate wind power prediction is essential for efficient utilization, equipment protection, and stable grid-connection of wind energy. Prediction interval (PI) provides a practical way to quantify the potential uncertainties of wind power. High coverage probability and narrow width are expected when constructing PI, however, the conflicts between these two objectives challenge the prediction accuracy. Therefore, a comprehensive PI quality evaluation strategy is presented in this paper to make a trade-off between coverage probability and interval width. It enables the prediction model to be trained by algorithms with high computational efficiency. Based on this evaluation strategy, a short-term wind power interval prediction method, which combines attention mechanism-based gated recurrent unit (Att-GRU), variational mode decomposition (VMD), and rolling fuzzy granulation (RFG), is proposed to construct high-quality PI and accurately capture the uncertainty of wind power. The validity of the proposed method is verified by comparative case studies on different datasets. Compared with other benchmark methods, the interval width is reduced by at least 22.66%, and the coverage probability and computation time have been promoted by at least 3.03% and 4.31% respectively.

Keywords: Wind power; Prediction interval (PI); Neural network (NN); Fuzzy information granulation (FIG); Variational mode decomposition (VMD); Gated recurrent unit (GRU) (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:251:y:2022:i:c:s0360544222007101

DOI: 10.1016/j.energy.2022.123807

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