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

## TL;DR

This paper presents a new method for predicting wind energy by combining oceanic climate data and time-lagged features, improving forecast accuracy for better grid integration.

## Contribution

The novel approach integrates oceanic indices and time-lagged features with machine learning for enhanced wind energy prediction.

## Key findings

- Incorporating lagged features significantly reduces RMSE and improves R2 scores in wind energy forecasts.
- MRMR-based feature selection identifies three key variables for accurate predictions.
- The method achieves an RMSE of ≈50 MWh and R2 ≈ 0.99.

## Abstract

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 R2 . 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 ≈50 MWh  and R2≈0.99 . The proposed approach delivers robust, computationally efficient forecasts, supporting more reliable grid operations and informing future integration of climate teleconnections in renewable energy forecasting.