Beyond wind speed: Integrating oceanic indices and time-lagged features for superior wind energy prediction.

Accurate wind energy forecasting is critical for integrating wind power into electrical grids due to its inherent variability and uncertainty. This study introduces a systematic framework that integrates large-scale oceanic climate indices and time-lagged features with advanced machine-learning models to enhance short-term wind power prediction. We evaluate four experimental configurations: (A) a baseline using only wind speed; (B) wind plus contemporaneous indices; (C) the addition of 1-12 month lags for both wind and index variables; and (D) MRMR-based feature selection applied to the full lagged set. A comprehensive benchmark using 25 state-of-the-art models is conducted on monthly data from the Pawan Danavi wind farm in Sri Lanka (2015-2019). Results reveal that raw indices alone can degrade forecast accuracy, while incorporating lagged features significantly reduces RMSE and enhances [Formula: see text]. MRMR pruning of the 156 lagged predictors distills the set to three key variables: current wind speed, a nine-month lag of the Atlantic Meridional Mode, and a six-month lagged wind speed. This yields a minimum RMSE of [Formula: see text] and [Formula: see text]. The proposed approach delivers robust, computationally efficient forecasts, supporting more reliable grid operations and informing future integration of climate teleconnections in renewable energy forecasting.