Hybrid Short-Term Wind Power Prediction Based on Markov Chain

被引：3
|
作者：
Zhou, Liangsong [1 ]
Zhou, Xiaotian [2 ]
Liang, Hao [2 ]
Huang, Mutao [1 ]
Li, Yi [3 ]
机构：
[1] Huazhong Univ Sci & Technol, Sch Elect & Elect Engn, Wuhan, Peoples R China
[2] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB, Canada
[3] Univ Washington, Coll Engn, Seattle, WA USA
来源：
FRONTIERS IN ENERGY RESEARCH | 2022年 / 10卷
关键词：
wind power prediction; combined model; Markov chain; chaotic time series; data-driven; NEURAL-NETWORK; SPEED;
D O I：
10.3389/fenrg.2022.899692
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
This article proposes a combined prediction method based on the Markov chain to realize precise short-term wind power predictions. First, three chaotic models are proposed for the prediction of chaotic time series, which can master physical principles in wind power processes and guide long-term prediction. Then, considering a mechanism switching between different physical models via a Markov chain, a combined model is constructed. Finally, the industrial data from a Chinese wind farm were taken as a study case, and the results validated the feasibility and superiority of the proposed prediction method.
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收藏
页数：7
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