Some improvements of wind speed Markov chain modeling

被引：49
|
作者：
Tang, Jie [1 ]
Brouste, Alexandre [2 ]
Tsui, Kwok Leung [1 ]
机构：
[1] City Univ Hong Kong, Dept Syst Engn & Engn Management, Hong Kong, Hong Kong, Peoples R China
[2] Univ Maine, Lab Manceau Math, F-72017 Le Mans, France
来源：
RENEWABLE ENERGY | 2015年 / 81卷
关键词：
Wind speed; Forecasting model; Markov chains modeling method; GENERATION; 1ST;
D O I：
10.1016/j.renene.2015.03.005
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
In this study, the traditional Markov chain method for wind speed modeling is analyzed and two improvements are introduced. New states categorization step and wind speeds simulation step are presented. They both take advantage of the empirical cumulative distribution function of the wind speed time series. Performances of the new method are tested in terms of modeling and short-term forecasting. The results suggest that this method overperforms the traditional one for modeling. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码：52 / 56
页数：5
相关论文
共 50 条
  • [31] Markov Chain Modeling of Pavement Surfacing
    Hassan, Rayya A.
    Lin, Oliver
    Thananjeyan, Annutha
    [J]. TRANSPORTATION RESEARCH RECORD, 2015, (2473) : 3 - 12
  • [32] Markov chain modeling of respiratory motion
    Christiansen, Eric
    Heath, Emily
    Xu, Tong
    [J]. MEDICAL PHYSICS, 2019, 46 (11) : 5395 - 5395
  • [33] Affective modeling based on Markov chain
    Wang, ZL
    Teng, SD
    Wang, L
    Liu, JW
    Xie, L
    [J]. 8TH WORLD MULTI-CONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL VII, PROCEEDINGS: APPLICATIONS OF INFORMATICS AND CYBERNETICS IN SCIENCE AND ENGINEERING, 2004, : 438 - 442
  • [34] Modeling Link Quality for High-Speed Railway Networks Based on Hidden Markov Chain
    Song, Jiayang
    Zhou, Huachun
    Quan, Wei
    Zheng, Tao
    Dong, Ping
    [J]. 2016 IEEE 83RD VEHICULAR TECHNOLOGY CONFERENCE (VTC SPRING), 2016,
  • [35] A Stochastic Approach for the State-Wise Forecast of Wind Speed Using Discrete-Time Markov Chain
    Yakasiri, Mounika
    Avrel, Joyce
    Sharma, Swathi
    Anuradha, M.
    Keshavan, B. K.
    [J]. PROCEEDINGS OF THE 2019 IEEE REGION 10 CONFERENCE (TENCON 2019): TECHNOLOGY, KNOWLEDGE, AND SOCIETY, 2019, : 575 - 580
  • [36] Modeling wind speed and wind power distributions in Rwanda
    Safari, Bonfils
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2011, 15 (02): : 925 - 935
  • [37] A Wind Power Time Series Modeling Method Based on the Improved Markov Chain Monte Carlo Method
    Zhu, Chenxi
    Zhang, Yan
    Yan, Zheng
    Zhu, Jinzhou
    Zhao, Teng
    [J]. Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, 2020, 35 (03): : 577 - 589
  • [38] Improvements to the Operational Tropical Cyclone Wind Speed Probability Model
    DeMaria, Mark
    Knaff, John A.
    Brennan, Michael J.
    Brown, Daniel
    Knabb, Richard D.
    DeMaria, Robert T.
    Schumacher, Andrea
    Lauer, Christopher A.
    Roberts, David P.
    Sampson, Charles R.
    Santos, Pablo
    Sharp, David
    Winters, Katherine A.
    [J]. WEATHER AND FORECASTING, 2013, 28 (03) : 586 - 602
  • [39] Modeling link quality for high-speed railway wireless networks based on hidden Markov chain
    Jiayang Song
    Huachun Zhou
    Wei Quan
    Tao Zheng
    Ping Dong
    [J]. EURASIP Journal on Wireless Communications and Networking, 2016
  • [40] Modeling link quality for high-speed railway wireless networks based on hidden Markov chain
    Song, Jiayang
    Zhou, Huachun
    Quan, Wei
    Zheng, Tao
    Dong, Ping
    [J]. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, 2016,
12345