Transfer Learning via Meteorological Clusters for Offshore Wind Forecasting

A new transfer learning framework addresses data scarcity for power forecasting at newly commissioned offshore wind farms. The method clusters power output based on covariate meteorological features, then uses an ensemble of expert models each trained on a specific cluster. This allows pre-trained models specialized in distinct weather patterns to adapt efficiently to new sites, capturing transferable, climate-dependent dynamics. The approach aims to ensure grid stability, reserve management, and efficient energy trading from the onset of farm operation.