Short-Term Wind Power Prediction Based on Variational Modal Decomposition and Quantile Convolution-Recurrent Neural Network

doi:10.11930/j.issn.1004-9649.202106030

Abstract

Abstract: Due to the randomness and intermittency of wind power, wind power forecasting requires not only accurate point forecasting, but also reliable interval and probabilistic forecasting to quantify the uncertainty of wind power. This paper proposes a probabilistic wind power forecasting method based on variational mode decomposition (VMD) and quantile convolution-recurrent neural network. Firstly, this method uses VMD to decompose the original wind power sequence into a series of modal components with different characteristics. Secondly, the convolutional neural network (CNN) is used to extract high-order features reflecting the dynamic changes of each modal component. Then, the quantile regression is performed by the long short-term memory (LSTM) recurrent neural network based on the high-order features to obtain the predicted values for different quantiles. Finally, the kernel density estimation (KDE) is employed to estimate the probability density curve of wind power. The effectiveness of the proposed method is verified with the example test using datasets from the wind farm in China.

Key words: wind power prediction, variational mode decomposition, convolutional neural network, long short-term memory recurrent neural network, quantile regression

SHA Jun, XU Yusen, LIU Chongchong, FENG Dingdong, XU Zheng, ZANG Haixiang. Short-Term Wind Power Prediction Based on Variational Modal Decomposition and Quantile Convolution-Recurrent Neural Network[J]. Electric Power, 2022, 55(12): 61-68.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202106030

https://www.electricpower.com.cn/EN/Y2022/V55/I12/61

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