A combination predicting methodology based on T-LSTNet_Markov for short-term wind power prediction

doi:10.1080/0954898X.2023.2213756

	A combination predicting methodology based on T-LSTNet_Markov for short-term wind power prediction
	Wang, Yongsheng 1,2; Wu, Yuhao 1; Xu, Hao 1,2; Chen, Zhen 1,2; Gao, Jing 3,4; Xu, ZhiWei 1,2,5; Li, Leixiao 1,2
	2023-05-20
发表期刊	NETWORK-COMPUTATION IN NEURAL SYSTEMS
ISSN	0954-898X
页码	23
摘要	Wind power has been valued by countries for its renewability and cleanness and has become most of the focus of energy development in all countries. However, due to the uncertainty and volatility of wind power generation, making the grid-connected wind power system presents some serious challenges. Improving the accuracy of wind power prediction has become the focus of current research. Therefore, this paper proposes a combined short-term wind power prediction model based on T-LSTNet_markov to improve prediction accuracy. First, perform data cleaning and data preprocessing operations on the original data. Second, forecast using T-LSTNet model in original wind power data. Finally, calculate the error between the forecast value and the actual value. The k-means++ method and Weighted Markov process are used to correct errors and to get the result of the final prediction. The data that are collected from a wind farm in Inner Mongolia Autonomous Region, China, are selected as a case study to demonstrate the effectiveness of the proposed combined models. The empirical results show that the prediction accuracy is further improved after correcting errors.
关键词	Wind power forecast T-LSTNet_markov model Deep learning Time series analysis
DOI	10.1080/0954898X.2023.2213756
收录类别	SCI
语种	英语
资助项目	Department of Science and Technology of Inner Mongolia[2020CG0073] ; National Natural Science Foundation of China[61962045] ; Natural Science Foundation of Inner Mongolia[2019MS03014] ; Research Program of Science and Technology at Universities of Inner Mongolia Autonomous Region[NJZY21321] ; Science and Technology Major Project of Inner Mongolia[2019ZD016]
WOS研究方向	Computer Science ; Engineering ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Neurosciences
WOS记录号	WOS:000996919800001
出版者	TAYLOR & FRANCIS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21474
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wu, Yuhao
作者单位	1.Inner Mongolia Univ Technol, Coll Data Sci & Applicat, Hohhot, Peoples R China 2.Res Ctr Big Data Based Software Serv, Inner Mongolia Autonomous Reg Engn & Technol, Hohhot, Peoples R China 3.Inner Mongolia Agr Univ, Coll Comp & Informat Engn, Hohhot, Peoples R China 4.Inner Mongolia Autonomous Reg Key Lab Big Data Res, Hohhot, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yongsheng,Wu, Yuhao,Xu, Hao,et al. A combination predicting methodology based on T-LSTNet_Markov for short-term wind power prediction[J]. NETWORK-COMPUTATION IN NEURAL SYSTEMS,2023:23.
APA	Wang, Yongsheng.,Wu, Yuhao.,Xu, Hao.,Chen, Zhen.,Gao, Jing.,...&Li, Leixiao.(2023).A combination predicting methodology based on T-LSTNet_Markov for short-term wind power prediction.NETWORK-COMPUTATION IN NEURAL SYSTEMS,23.
MLA	Wang, Yongsheng,et al."A combination predicting methodology based on T-LSTNet_Markov for short-term wind power prediction".NETWORK-COMPUTATION IN NEURAL SYSTEMS (2023):23.