Wind power production data at temporal resolutions of a few minutes exhibits 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 to accommodate
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 1-step-ahead point forecasting of 10-minute resolution time-series
of wind power at two large offshore wind farms. They are favourably compared against persistence and
AutoRegressive (AR) 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.