  There is increasing interest in very short-term and higher-resolution wind
power forecasting (from minutes to hours ahead), especially offshore.
Statistical methods are of utmost relevance, since weather forecasts cannot be
informative for those lead times. Those approaches ought to account for the
fact that wind power generation as a stochastic process is nonstationary,
double-bounded (by zero and the nominal power of the turbine) and non-linear.
Accommodating those aspects may lead to improving both point and probabilistic
forecasts. We propose here to focus on generalized logit-normal distributions,
which are naturally suitable and flexible for double-bounded and non-linear
processes. Relevant parameters are estimated via maximum likelihood inference.
Both batch and online versions of the estimation approach are described -- the
online version permitting to additionally handle non-stationarity through the
variation of distribution parameters. The approach is applied and analysed on
the test case of the Anholt offshore wind farm in Denmark, with emphasis placed
on 10-min-ahead point and probabilistic forecasts.

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