WindDragon: Enhancing wind power forecasting with Automated Deep Learning

arXiv:2402.14385v1 Announce Type: new
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) windpower forecasting at a national level. The method leverages Automated Deep Learning combined with …

abstract accurate forecasting arxiv automated carbon challenge cs.lg deep learning emissions energy forecasting grid integration operators physics.ao-ph power stat.ml type uncertainty wind