  Renewable energy sources provide a constantly increasing contribution to the
total energy production worldwide. However, the power generation from these
sources is highly variable due to their dependence on meteorological
conditions. Accurate forecasts for the production at various temporal and
spatial scales are thus needed for an efficiently operating electricity market.
In this article - part 1 - we propose fully probabilistic prediction models for
spatially aggregated wind power production at an hourly time scale with lead
times up to several days using weather forecasts from numerical weather
prediction systems as covariates. After an appropriate cubic transformation of
the power production, we build up a multivariate Gaussian prediction model
under a Bayesian inference framework which incorporates the temporal error
correlation. In an application to predict wind production in Germany, the
method provides calibrated and skillful forecasts. Comparison is made between
several formulations of the correlation structure.
