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

Chen Yu, Yusheng Xue*, Fushuan Wen, Zhaoyang Dong, K. P. Wong, Kang Li

*Corresponding author for this work

Research output: Contribution to journalArticle

17 Citations (Scopus)


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 languageChinese
Pages (from-to)5-11
Number of pages7
JournalDianli Xitong Zidonghua/Automation of Electric Power Systems
Issue number8
Publication statusPublished - 25 Apr 2015


  • Classification of time-series tendency
  • Offline optimization
  • Online matching
  • Time series features
  • Wind power prediction

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Energy Engineering and Power Technology

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