Achieving net-zero carbon emissions by 2050 necessitates the integration of substantial wind power capacity into national power grids. However, the inherent variability and uncertainty of wind energy present significant challenges for grid operators, particularly in maintaining system stability and balance. Accurate short-term forecasting of wind power is therefore essential. This article introduces an innovative framework for regional wind power forecasting over short-term horizons (1–6 h), employing a novel Automated Deep Learning regression framework called WindDragon. Specifically designed to process wind speed maps, WindDragon automatically creates Deep Learning models leveraging Numerical Weather Prediction (NWP) data to deliver state-of-the-art wind power forecasts. We conduct extensive evaluations on data from France for the year 2020, benchmarking WindDragon against a diverse set of baselines, including both deep learning and traditional methods. The results demonstrate that WindDragon achieves substantial improvements in forecast accuracy over the considered baselines, highlighting its potential for enhancing grid reliability in the face of increased wind power integration.