The Journal of China Universities of Posts and Telecommunications ›› 2021, Vol. 28 ›› Issue (1): 41-51.doi: 10.19682/j.cnki.1005-8885.2021.0004

• Artificial intelligence • Previous Articles     Next Articles

Wind speed prediction based on nested shared weight long short-term memory network

  

  1. School of Computer and Communication Engineering, Qinhuangdao Branch of Northeast University, Qinhuangdao 066000, China
  • Received:2020-05-12 Revised:2020-08-09 Online:2021-02-28 Published:2021-03-28
  • Contact: Ying-Hua wuHAN E-mail:yhhan723@126.com
  • Supported by:

    This work was supported by the National Key Research and Development Programe of China ( 2016YFB0901900 ), the National Natural Science Foundation of China (U1908213), the Fundamental the Research Funds for the Central Universities (N182303037), Colleges and Universities in Hebei Province Science Research Program ( QN2020504), the Foundation of

    Northeastern University at Qinhuangdao (XNB201803).

Abstract:

With the expansion of wind speed data sets, decreasing model training time is of great significance to the time cost of wind speed prediction. And imperfection of the model evaluation system also affect the wind speed prediction. To address these challenges, a hybrid method based on feature extraction, nested shared weight long short-term memory (NSWLSTM) network and Gaussian process regression (GPR) was proposed. The feature extraction of wind speed promises the best performance of the model. NSWLSTM model reduces the training time of long short-term memory (LSTM) network and improves the prediction accuracy. Besides, it adopted a method combined NSWLSTM with GPR (NSWLSTMGPR) to provide the probabilistic prediction of wind speed. The probabilistic prediction can provide information that deviates from the predicted value, which is conducive to risk assessment and optimal scheduling. The simulation results show that the proposed method can obtain high-precision point prediction, appropriate prediction interval and reliable probabilistic prediction results with shorter training time on the wind speed prediction.

Key words: wind speed prediction| feature extraction| long short-term memory network (LSTM)| shared weight| forecast uncertainty.