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

共 50 条

[41] Research on short-term offshore wind power prediction based on sodar wind measurement
Leng Xuemin
Gao Yang
Ding Yujie
Li Qifeng
Sui Yuxuan
Li Yujia
[J]. 2020 CHINESE AUTOMATION CONGRESS (CAC 2020), 2020, : 1341 - 1344
[42] A hybrid strategy of short term wind power prediction
Peng, Huaiwu
Liu, Fangrui
Yang, Xiaofeng
[J]. RENEWABLE ENERGY, 2013, 50 : 590 - 595
[43] Short-Term Power Allocation for Slowly Fading Channels Based on Markov Prediction
Sharifkhani, Ali
Beaulieu, Norman C.
[J]. 2009 IEEE VEHICULAR TECHNOLOGY CONFERENCE, VOLS 1-5, 2009, : 2334 - 2339
[44] A New ANN-Based Methodology for Very Short-Term Wind Speed Prediction Using Markov Chain Approach
Kani, S. A. Pourmousavi
Riahy, G. H.
[J]. 2008 IEEE ELECTRICAL POWER AND ENERGY CONFERENCE, 2008, : 566 - 571
[45] Short-Term Wind Speed and Wind Power Prediction Based on Meteorological Model Modification
Liu, Tianze
Zhao, Yan
Sun, Gang
Ma, Yanjuan
[J]. PROCEEDINGS OF 2020 IEEE 9TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS'20), 2020, : 742 - 747
[46] Short-term wind power prediction and error analysis
Ma, Rui
Wang, Lingling
Hu, Shuju
[J]. RENEWABLE ENERGY AND ENVIRONMENTAL TECHNOLOGY, PTS 1-6, 2014, 448-453 : 1851 - 1857
[47] Short-term prediction of wind power with a clustering approach
Kusiak, Andrew
Li, Wenyan
[J]. RENEWABLE ENERGY, 2010, 35 (10) : 2362 - 2369
[48] A review on short-term and ultra-short-term wind power prediction
Xue, Yusheng
Yu, Chen
Zhao, Junhua
Li, Kang
Liu, Xueqin
Wu, Qiuwei
Yang, Guangya
[J]. Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2015, 39 (06): : 141 - 151
[49] A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal
Pousinho, H. M. I.
Mendes, V. M. F.
Catalao, J. P. S.
[J]. ENERGY CONVERSION AND MANAGEMENT, 2011, 52 (01) : 397 - 402
[50] Very Short-Term Prediction of Wind Farm Power: An Advanced Hybrid Intelligent Approach
Peri, Ramya M.
Mandal, Paras
Hague, Ashraf U.
Tseng, Bill
[J]. 2015 51ST IEEE INDUSTRY APPLICATIONS SOCIETY ANNUAL MEETING, 2015,

← 1 2 3 4 5 →