Very short-term spatio-temporal wind power prediction using a censored Gaussian field

Anastassia Baxevani, Amanda Lenzi*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


Wind power is a renewable energy resource, that has relatively cheap installation costs and it is highly possible that will become the main energy resource in the near future. Wind power needs to be integrated efficiently into electricity grids, and to optimize the power dispatch, techniques to predict the level of wind power and the associated variability are critical. Ideally, one would like to obtain reliable probability density forecasts for the wind power distributions. We aim at contributing to the literature of wind power prediction by developing and analysing a spatio-temporal methodology for wind power production, that is tested on wind power data from Denmark. We use anisotropic spatio-temporal correlation models to account for the propagation of weather fronts, and a transformed latent Gaussian field model to accommodate the probability masses that occur in wind power distribution due to chains of zeros. We apply the model to generate multi-step ahead probability predictions for wind power generated at both locations where wind farms already exist but also to nearby locations.
Original languageEnglish
JournalStochastic Environmental Research and Risk Assessment
Issue number4
Pages (from-to)931-948
Publication statusPublished - 2018


  • Wind power
  • Spatio-temporal model
  • Kriging equations
  • Gaussian transformed model
  • Covariance function

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