A gated recurrent unit neural networks based wind speed error correction model for short-term wind power forecasting

• Published in: Neurocomputing (Amsterdam) 2019-11, Vol.365, p.54-61
• Main Authors: Ding, Min, Zhou, Hao, Xie, Hua, Wu, Min, Nakanishi, Yosuke, Yokoyama, Ryuichi
• Format: Article
• Language: English
• Subjects: Error correction model; Feature extraction; Gated recurrent unit neural networks; Short-term wind power forecasting
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2019.07.058

With the growing penetration of wind power, the wind power forecasting is fundamental in aiding the grid scheduling and electricity trading. In this paper, a numerical weather prediction wind speed error correction model based on gated recurrent unit neural networks is proposed for short-term wind power forecasting. Firstly, the standard deviation of numerical weather prediction wind speed error is extracted as weights, and these weights are rearranged according to the numerical weather prediction wind speed time series to get the weight time series. Then, the bidirectional gated recurrent unit neural networks based error correction model is proposed to correct error of numerical weather prediction wind speed with the inputs as numerical weather prediction wind speed, trend and detail terms of the weight time series. The wind power curve model is applied to forecast short-term wind power by using corrected numerical weather prediction wind speed. Finally, the effectiveness of the proposed method is compared with benchmark models by using actual data of wind farm, and the results show that the proposed model outperforms these benchmark models.