An ultra-short-term wind power prediction method using “offline classification and optimization, online model matching” based on time series features

C. Yu, Y. Xue, F. Wen, Z. Dong, Kitpo Wong, K. Li

    Research output: Contribution to journalArticle

    12 Citations (Scopus)


    ©2015 State Grid Electric Power Research Institute Press The applicability of ultra-short-term wind power prediction (USTWPP) models is reviewed. The USTWPP method proposed extracts featrues from historical data of wind power time series (WPTS), and classifies every short WPTS into one of several different subsets well defined by stationary patterns. All the WPTS that cannot match any one of the stationary patterns are sorted into the subset of nonstationary pattern. Every above WPTS subset needs a USTWPP model specially optimized for it offline. For on-line application, the pattern of the last short WPTS is recognized, then the corresponding prediction model is called for USTWPP. The validity of the proposed method is verified by simulations.
    Original languageEnglish
    Pages (from-to)5-11
    JournalDianli Xitong Zidonghua/Automation of Electric Power Systems
    Issue number8
    Publication statusPublished - 2015


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