Very-short-term probabilistic wind power forecasts by sparse vector autoregression

Jethro Dowell, Pierre Pinson

Research output: Contribution to journalArticlepeer-review

131 Citations (Scopus)
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A spatio-temporal method for producing very-short-term parametric probabilistic wind power forecasts at a large number of locations is presented. Smart grids containing tens, or hundreds, of wind generators require skilled very-short-term forecasts to operate effectively, and spatial information is highly desirable. In addition, probabilistic forecasts are widely regarded as necessary for optimal power system management as they quantify the uncertainty associated with point forecasts. Here we work within a parametric framework based on the logit-normal distribution and forecast its parameters. The location parameter for multiple wind farms is modelled as a vector-valued spatio-temporal process, and the scale parameter is tracked by modified exponential smoothing. A state-of-the-art technique for fitting sparse vector autoregressive models is employed to model the location parameter and demonstrates numerical advantages over conventional vector autoregressive models. The proposed method is tested on a dataset of 5 minute mean wind power generation at 22 wind farms in Australia. 5-minute-ahead forecasts are
produced and evaluated in terms of point and probabilistic forecast skill scores and calibration. Conventional autoregressive and vector autoregressive models serve as benchmarks.
Original languageEnglish
Pages (from-to)763-770
Number of pages8
JournalIEEE Transactions on Smart Grid
Issue number2
Early online date12 May 2015
Publication statusPublished - 31 Mar 2016


  • probabilistic forecasting
  • wind power
  • power system operations
  • renewable energy


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