Wind power predictions from nowcasts to 4-hour forecasts: a learning approach with variable selection

The fast development of renewable energies is a necessity to mitigate climate changes [22]. Wind energy has developed rapidly over the past three decades, with an average annual growth rate of 23.6% between 1990 and 2016 [17], and is now considered as a mature technology. The share of renewable energies in global electricity generation reached 29% in 2020, and is expected to keep growing fast in coming years [18] which raises a number of challenges, stemming from the variability and spatial distribution of the resource. Then, in order to facilitate the dynamic management of electricity networks, forecasts of wind energy require continual improvement. Short timescales, from a few minutes to a few hours, are of particular importance for operations. To produce forecasts, one can rely on several distinct sources of information. On timescales of half a day to about a week, deterministic weather forecasts provide a representation on a grid of the atmospheric state, including wind speed near the surface. The skill of such numerical weather forecasts (NWP) models has continually increased over the past decades [2], while their spatial resolution has also grown finer (down to few km).