南方科技大学知识苑(SUSTech KC): Short-term wind power prediction method based on CEEMDAN-GWO-Bi-LSTM

题名	Short-term wind power prediction method based on CEEMDAN-GWO-Bi-LSTM
作者	Sun，Hongbin 1; Cui，Qing 1; Wen，Jingya 2; Kou，Lei 2; Ke，Wende3
通讯作者	Kou，Lei
发表日期	2024-06-01
DOI	10.1016/j.egyr.2024.01.021
发表期刊	Energy Reports 影响因子和分区
EISSN	2352-4847
卷号	11页码:1487-1502
摘要	In order to improve the short-term prediction accuracy of wind power and provide the basis for power grid dispatching, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) -grey wolf optimization (GWO) -bidirectional long short-term memory network (Bi-LSTM) prediction model is proposed to predict the short-term output power of wind farms. Firstly, the original wind power data is preprocessed, and then the original wind power data is decomposed into components that are easy to extract features by using CEEMDAN. The Bi-LSTM prediction model is established for each component, and then the grey wolf optimization algorithm is used to optimize the parameters of the Bi-LSTM model. The optimized hyperparameters are brought into the Bi-LSTM model to output the prediction results of each component. Finally, the prediction results of each component are superimposed and reconstructed to obtain the final prediction results of wind power. The simulation analysis of the power data of a wind farm in Gansu Province shows that the CEEMDAN-GWO-Bi-LSTM model has better accuracy in short-term wind power prediction.
关键词	Adaptive noise Bidirectional long short-term memory network Complete ensemble empirical mode decomposition Grey wolf optimization Short-term prediction Wind power
相关链接	[Scopus记录]
收录类别	SCI ; EI
语种	英语
学校署名	其他
Scopus记录号	2-s2.0-85182884668
来源库	Scopus
引用统计	被引频次[WOS]：7
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/701292
专题	工学院_机械与能源工程系
作者单位	1.School of Electrical Engineering,Changchun Institute of Technology,Changchun,130012,China 2.Institute of Oceanographic Instrumentation,Qilu University of Technology (Shandong Academy of Sciences),Qingdao,266075,China 3.Department of Mechanical and Energy Engineering,Southern University of Science and Technology,Shenzhen,518055,China
推荐引用方式 GB/T 7714	Sun，Hongbin,Cui，Qing,Wen，Jingya,et al. Short-term wind power prediction method based on CEEMDAN-GWO-Bi-LSTM[J]. Energy Reports,2024,11:1487-1502.
APA	Sun，Hongbin,Cui，Qing,Wen，Jingya,Kou，Lei,&Ke，Wende.(2024).Short-term wind power prediction method based on CEEMDAN-GWO-Bi-LSTM.Energy Reports,11,1487-1502.
MLA	Sun，Hongbin,et al."Short-term wind power prediction method based on CEEMDAN-GWO-Bi-LSTM".Energy Reports 11(2024):1487-1502.