EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning

Xiaosheng Peng, Hongyu Wang, Jianxun Lang, Wenze Li, Qiyou Xu, Zuowei Zhang, Tao Cai, Shanxu Duan, Fangjie Liu and Chaoshun Li

Energy, 2021, vol. 220, issue C

Abstract: Effective wind-power prediction enhances the adaptability of a wind power system to the instability of wind power, which is beneficial for load and frequency regulation, helping to convert wind power to electricity and connect wind power to the grid safely. Moreover, the use of numerical weather prediction (NWP) to predict the probability results of wind power is a matter of general concern in the field of wind power prediction, and deep neural networks have become an indispensable research tool. In this study, a new neural-network prediction model called EALSTM-QR was developed for wind-power prediction considering the input of NWP and the deep-learning method. In the model, there are four main levels: Encoder, Attention, bidirectional long short-term memory (LSTM), and quantile regression (QR). The combination inputs contain historical wind-power data and the features extracted and obtained from the NWP through the Encoder and Attention levels. The bidirectional LSTM is used to generate wind-power time-series probability prediction results. The QR method and confidence interval limits are used to obtain the final prediction intervals. The proposed method was compared with several interval prediction models and probability prediction models based on neural networks for wind-power prediction by using datasets from wind farms in China. The results indicated that the proposed EALSTM-QR has good accuracy and reliability for the prediction of intervals and probabilities.

Keywords: Wind-power prediction; EALSTM-QR; Bidirectional LSTM; Quantile regression (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:220:y:2021:i:c:s0360544220327997

DOI: 10.1016/j.energy.2020.119692

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