Adaptive modelling and forecasting of offshore wind power fluctuations with Markov-switching autoregressive models

Publication: Research - peer-reviewJournal article – Annual report year: 2011

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Wind power production data at temporal resolutions of a few minutes exhibit successive periods with fluctuations of various dynamic nature and magnitude, which cannot be explained (so far) by the evolution of some explanatory variable. Our proposal is to capture this regime-switching behaviour with an approach relying on Markov-switching autoregressive (MSAR) models. An appropriate parameterization of the model coefficients is introduced, along with an adaptive estimation method allowing accommodation of long-term variations in the process characteristics. The objective criterion to be recursively optimized is based on penalized maximum likelihood, with exponential forgetting of past observations. MSAR models are then employed for one-step-ahead point forecasting of 10 min resolution time series of wind power at two large offshore wind farms. They are favourably compared against persistence and autoregressive models. It is finally shown that the main interest of MSAR models lies in their ability to generate interval/density forecasts of significantly higher skill.
Original languageEnglish
JournalJournal of Forecasting
Publication date2012
Volume31
Issue4
Pages281–313
ISSN0277-6693
DOIs
StatePublished
CitationsWeb of Science® Times Cited: 17

Keywords

  • Wind power forecasting, Regime switching, Adaptive estimation, Point forecasting, Interval forecasting
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