An Innovative Hybrid Algorithm for Very Short-Term Wind Speed Prediction Using Linear Prediction and Markov Chain Approach

被引：18
作者：
Kani, S. A. Pourmousavi [1 ]
Riahy, G. H. [2 ]
Mazhari, D. [3 ]
机构：
[1] Montana State Univ, Elect & Comp Engn Dept, Bozeman, MT 59715 USA
[2] Amirkabir Univ Technol, Dept Elect Engn, Ctr Excellence Power Syst, Wind Energy Lab, Tehran, Iran
[3] Noshirvani Inst Technol, Elect & Comp Engn Dept, Babol Sar, Mazandaran, Iran
来源：
INTERNATIONAL JOURNAL OF GREEN ENERGY | 2011年 / 8卷 / 02期
关键词：
Linear prediction; Markov chain; Maximum percentage error; Mean absolute percentage error; Wind speed prediction; GENERATION; MODEL;
D O I：
10.1080/15435075.2010.548887
中图分类号：
O414.1 [热力学];
学科分类号：
摘要：
A new hybrid algorithm using linear prediction and Markov chain is proposed in order to facilitate very short-term wind speed prediction. First, the Markov chain transition probability matrix is calculated. Then, linear prediction method is applied to predict very short-term values. Finally, the results are modified according to the long-term pattern by a nonlinear filter. The results from proposed method are compared by linear prediction method, persistent method and actual values. It is shown that the prediction-modification processes improves very short-term predictions, by reducing the maximum percentage error and mean absolute percentage error, while it retains simplicity and low CPU time and improvement in uncertainty of prediction.
引用
收藏
页码：147 / 162
页数：16
相关论文
共 50 条
  • [31] Short-Term Wind Speed Forecasting Based on Data Preprocessing and Improved Hybrid Prediction Network
    Chen, Gonggui
    Li, Lijun
    Qin, Feng
    2020 CHINESE AUTOMATION CONGRESS (CAC 2020), 2020, : 734 - 738
  • [32] Short-Term Wind Speed Prediction Model of LS-SVM Based on Genetic Algorithm
    Han Xiaojuan
    Zhang Xilin
    Gao Bo
    ADVANCED TECHNOLOGY IN TEACHING - PROCEEDINGS OF THE 2009 3RD INTERNATIONAL CONFERENCE ON TEACHING AND COMPUTATIONAL SCIENCE (WTCS 2009), VOL 1: INTELLIGENT UBIQUITIOUS COMPUTING AND EDUCATION, 2012, 116 : 221 - +
  • [33] Short-term wind speed prediction using Extended Kalman filter and machine learning
    Hur, Sung-ho
    ENERGY REPORTS, 2021, 7 : 1046 - 1054
  • [34] Short-Term Wind Power Prediction Based on a Hybrid Markov-Based PSO-BP Neural Network
    Wang, Chia-Hung
    Zhao, Qigen
    Tian, Rong
    ENERGIES, 2023, 16 (11)
  • [35] Wind Speed Prediction and Visualization Using Long Short-Term Memory Networks (LSTM)
    Ehsan, Amimul
    Shahirinia, Amir
    Zhang, Nian
    Oladunni, Timothy
    2020 10TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND TECHNOLOGY (ICIST), 2020, : 234 - 240
  • [36] Short-Term Wind Speed Forecasting Based on Hybrid MODWT-ARIMA-Markov Model
    Yousuf, Muhammad Uzair
    Al-Bahadly, Ibrahim
    Avci, Ebubekir
    IEEE ACCESS, 2021, 9 (09): : 79695 - 79711
  • [37] Performance Comparison of Models for Fast Short-term Wind Speed Prediction
    Mao, Meiqin
    Chen, Shilong
    Cao, Yu
    Zhao, Yongchao
    Chang, Liuchen
    2013 4TH IEEE INTERNATIONAL SYMPOSIUM ON POWER ELECTRONICS FOR DISTRIBUTED GENERATION SYSTEMS (PEDG), 2013,
  • [38] Extreme learning Kalman filter for short-term wind speed prediction
    Wang, Hairong
    FRONTIERS IN ENERGY RESEARCH, 2023, 10
  • [39] Multistep short-term wind speed prediction with rank pooling and fast Fourier transformation
    Shu, Hailong
    Song, Weiwei
    Song, Zhen
    Guo, Huichuang
    Li, Chaoqun
    Wang, Yue
    WIND ENERGY, 2024, 27 (07) : 667 - 694
  • [40] Short-Term Wind Speed Prediction Based on LS-SVM
    Han, Xiaojuan
    Chen, Fang
    Cao, Hui
    Li, Xiangjun
    Zhang, Xilin
    PROCEEDINGS OF THE 10TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA 2012), 2012, : 3200 - 3204
12345