A sparsified vector autoregressive model for short-term wind farm power forecasting

Miao He, Vijay Vittal, Junshan Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


Short-term wind farm power forecasting is studied by exploiting the spatio-temporal correlation between individual turbine's power output. A multivariate time series model for wind farm power generation is developed by using vector autoregression (VAR). In order to avoid the possible over-fitting issues caused by a large number of autoregressive coefficients and the impact on the forecasting performance of VAR models, a sparsified autoregressive coefficient matrix is constructed by utilizing the information on wind direction, wind speed and wind farm's layout. Then, the VAR model parameters are obtained through maximum likelihood estimation of real-time measurement data, by taking into account the sparse structure of the autoregressive coefficient matrix. The proposed approach is compared with univariate autoregressive models through numerical experiments, resulting in significant improvement, which is attributed to the turbine-level correlation captured by the developed VAR model.

Original languageEnglish (US)
Title of host publicationIEEE Power and Energy Society General Meeting
PublisherIEEE Computer Society
ISBN (Print)9781467380409
StatePublished - Sep 30 2015
EventIEEE Power and Energy Society General Meeting, PESGM 2015 - Denver, United States
Duration: Jul 26 2015Jul 30 2015


OtherIEEE Power and Energy Society General Meeting, PESGM 2015
Country/TerritoryUnited States


  • Multivariate time series analysis
  • short-term wind power forecasting
  • vector autoregression
  • wind farm

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering


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