  The increasing penetration level of energy generation from renewable sources
is demanding for more accurate and reliable forecasting tools to support
classic power grid operations (e.g., unit commitment, electricity market
clearing or maintenance planning). For this purpose, many physical models have
been employed, and more recently many statistical or machine learning
algorithms, and data-driven methods in general, are becoming subject of intense
research. While generally the power research community focuses on power
forecasting at the level of single plants, in a short future horizon of time,
in this time we are interested in aggregated macro-area power generation (i.e.,
in a territory of size greater than 100000 km^2) with a future horizon of
interest up to 15 days ahead. Real data are used to validate the proposed
forecasting methodology on a test set of several months.
