Journal of Shanghai Jiao Tong University ›› 2021, Vol. 55 ›› Issue (9): 1080-1086.doi: 10.16183/j.cnki.jsjtu.2020.433

Special Issue: 《上海交通大学学报》2021年12期专题汇总专辑 《上海交通大学学报》2021年“能源与动力工程”专题

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Short-Term Wind Speed Forecasting Model Based on Mutual Information and Recursive Neural Network

WANG Yana, CHEN Yaorana, HAN Zhaolonga,b,c, ZHOU Daia,b,c(), BAO Yana,b,c   

  1. a. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    b. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    c. Key Laboratory of Hydrodynamics of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
  • Received:2020-12-25 Online:2021-09-28 Published:2021-10-08
  • Contact: ZHOU Dai


The volatility and randomness of wind speed have caused potential safety hazards to wind power grid integration. Improving wind speed forecasting is crucial to the stability of wind power systems and the development of wind energy. A novel short-term wind speed forecasting model (MI-RNN) was proposed based on mutual information (MI) and recursive neural network (RNN). In this model, the MI theory was introduced to select the optimal length of historical wind speed sequence (τ), and the method of using each τ step to forecast wind speed at the next time point was adopted to input the historical wind speed data into RNN for model training. The final wind speed forecasting result was output by the trained RNN model. Besides, the MI-RNN model was applied to the wind speed dataset collected from a wind farm and the forecasting accuracy was compared with that of the traditional wind forecasting methods. The results show that the MI-RNN model has achieved a higher forecasting accuracy compared with the commonly used wind farm wind speed forecasting methods, and can accurately forecast the future wind direction, which is expected to be applied to wind speed forecasting of wind farms with spatial dimensions.

Key words: wind speed forecasting, wind energy, mutual information (MI), deep learning, recurrent neural network (RNN), wind farm

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