WindDragon: Enhancing wind power forecasting with Automated Deep
  Learning
Julie Keisler Etienne Le Naour
Achieving net zero carbon emissions by 2050 requires the integration of
increasing amounts of wind power into power grids. This energy source poses a
challenge to system operators due to its variability and uncertainty.
Therefore, accurate forecasting of wind power is critical for grid operation
and system balancing. This paper presents an innovative approach to short-term
(1 to 6 hour horizon) windpower forecasting at a national level. The method
leverages Automated Deep Learning combined with Numerical Weather Predictions
wind speed maps to accurately forecast wind power.