Ultra-Short-Term Wind Power Prediction Based on Bidirectional Gated Recurrent Unit and Transfer Learning

Frontiers in Energy Research

Fei Tang¹  Yu Li¹  Wei Ru²  Dong Xie²  Feng Zhu²  Gang Luo²  Weiwen Qi²  Meiya Song²  Jun Zhang³  Wenjin Chen³ 

[1] School of Electrical and Automation, Wuhan University, Wuhan, China;State Grid Shaoxing Power Supply Company, Shaoxing, China;State Grid Zhejiang Electric Power Company, Ltd., Hangzhou, China;

关键词: bidirectional gated recurrent unit;    transfer learning;    target domain;    wind power;    wind power forecasting;   

DOI  :  10.3389/fenrg.2021.808116

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown