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

doi:10.19682/j.cnki.1005-8885.2021.0004

中国邮电高校学报(英文) ›› 2021, Vol. 28 ›› Issue (1): 41-51.doi: 10.19682/j.cnki.1005-8885.2021.0004

• Artificial Intelligence • 上一篇下一篇

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

韩凤全¹,韩英华²,陆靖¹,赵强³

1. 东北大学秦皇岛分校
2. 东北大学
3.

收稿日期:2020-05-12 修回日期:2020-08-09 出版日期:2021-02-28 发布日期:2021-03-28
通讯作者: 韩英华 E-mail:yhhan723@126.com
基金资助:
国家重点研究发展计划;国家自然科学基金;中央大学基础研究基金;东北大学秦皇岛分校基金

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

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).

摘要/Abstract

摘要：

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.

关键词:

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

Abstract:

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

Han Fengquan, Han Yinghua , Lu Jing, Zhao Qiang. Wind speed prediction based on nested shared weight long short-term memory network[J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28(1): 41-51.

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

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

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