PROBABILITY PREDICTION OF WIND POWER BASED ON QR-NFGLSTM AND KERNEL DENSITY ESTIMATION

Wang Xiaodong; Ju Bangguo; Liu Yingming; Zang Tonglin

doi:10.19912/j.0254-0096.tynxb.2020-0478

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (2) : 479-485. DOI: 10.19912/j.0254-0096.tynxb.2020-0478

PROBABILITY PREDICTION OF WIND POWER BASED ON QR-NFGLSTM AND KERNEL DENSITY ESTIMATION

Wang Xiaodong, Ju Bangguo, Liu Yingming, Zang Tonglin

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Abstract

This paper proposes a wind power probability prediction method in order to improve the accuracy of wind power probability prediction and reduce the training time of long short-term memory network. This method is based on quantile regression combined with a new forget gate long short-term memory （NFGLSTM） network and kernel density estimation. The structure of long short-term memory network is improved and a new forget gate structure is proposed, which is used to shorten the training time. A combined forecasting model is established based on quantile regression and NGFLSTM network. So, the point prediction value of wind power and the prediction interval under a certain confidence are obtained. The kernel density estimation of the Cosine kernel function is used to solve probability density function of the predicted value. The case study shows that the proposed method can shorten the training time of long and short-term memory networks and improve the probability prediction accuracy.

Key words

wind power / prediction / long short-term memory / quantile regression / kernel density estimation

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Wang Xiaodong, Ju Bangguo, Liu Yingming, Zang Tonglin. PROBABILITY PREDICTION OF WIND POWER BASED ON QR-NFGLSTM AND KERNEL DENSITY ESTIMATION[J]. Acta Energiae Solaris Sinica. 2022, 43(2): 479-485 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0478

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