Ultra-Short-Term Wind Power Prediction Based on Bidirectional Gated Recurrent Unit and Transfer Learning - Chen, Wenjin; Qi, Weiwen; Li, Yu; Zhang, Jun; Zhu, Feng; Xie, Dong; Ru, Wei; Luo, Gang; Song, Meiya; Tang, Fei (Frontiers in Energy Research, 2021)
The wind power forecasting (WPF) is imperative to the control and dispatch of 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 dimension to match output vector. Furthermore, a transfer learning (TL) strategy is utilized to establish prediction model of target wind farm with fewer data and less training time based on source wind farm. The proposed method is validated on two wind farms located in China and the results proves its superior prediction performance compared with other approaches.