Time series wind power forecasting based on variant Gaussian process and TLBO

Juan Yan, Kang Li, Er-Wei Bai, Zhile Yang, Aoife Foley

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

Due to the variability and stochastic nature of wind power system, accurate wind power forecasting has an important role in developing reliable and economic power system operation and control strategies. As wind variability is stochastic, Gaussian Process regression has recently been introduced to capture the randomness of wind energy. However, the disadvantages of Gaussian Process regression include its computation complexity and incapability to adapt to time varying time-series systems. A variant Gaussian Process for time series forecasting is introduced in this study to address these issues. This new method is shown to be capable of reducing computational complexity and increasing prediction accuracy. It is further proved that the forecasting result converges as the number of available data approaches innite. Further, a teaching learning based optimization (TLBO) method is used to train the model and to accelerate
the learning rate. The proposed modelling and optimization method is applied to forecast both the wind power generation of Ireland and that from a single wind farm to show the eectiveness of the proposed method.
Original languageEnglish
Pages (from-to)135-144
Number of pages10
JournalNeurocomputing
Volume189
Early online date07 Jan 2016
DOIs
Publication statusPublished - 12 May 2016

Keywords

  • Gaussian Process
  • Model consistency
  • TLBO
  • Wind power forecasting

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