Author:
Chen Wenjin,Qi Weiwen,Li Yu,Zhang Jun,Zhu Feng,Xie Dong,Ru Wei,Luo Gang,Song Meiya,Tang Fei
Abstract
Wind power forecasting (WPF) is imperative to the control and dispatch of the power grid. Firstly, an ultra-short-term prediction method based on multilayer bidirectional gated recurrent unit (Bi-GRU) and fully connected (FC) layer is proposed. The layers of Bi-GRU extract the temporal feature information of wind power and meteorological data, and the FC layer predicts wind power by changing dimensions to match the output vector. Furthermore, a transfer learning (TL) strategy is utilized to establish the prediction model of a target wind farm with fewer data and less training time based on the source wind farm. The proposed method is validated on two wind farms located in China and the results prove its superior prediction performance compared with other approaches.
Funder
Science and Technology Project of State Grid
Subject
Economics and Econometrics,Energy Engineering and Power Technology,Fuel Technology,Renewable Energy, Sustainability and the Environment
Cited by
8 articles.
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