WindDragon: enhancing wind power forecasting with automated deep learning (Papers Track)
Julie Keisler (INRIA, EDF R&D); Etienne Le Naour (Sorbonne University, EDF R&D)
Abstract
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) wind power forecasting at a national level. The method leverages Automated Deep Learning combined with Numerical Weather Predictions wind speed maps to accurately forecast wind power.