WindDragon: Enhancing wind power forecasting with Automated Deep Learning
This addresses the challenge of wind power variability for grid operators, but appears incremental as it applies an existing automated method to a specific domain.
The paper tackled the problem of short-term wind power forecasting at a national level to support grid integration, achieving accurate predictions using Automated Deep Learning with Numerical Weather Predictions.
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.