Hybrid Short-Term Wind Power Prediction Based on Markov Chain

被引：2

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.

引用

页数：7

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