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

Namal Rathnayake, Mahesh Yadev, Jeevani Jayasinghe, Upaka Rathnayake, Masashi Minamide and Yukinobu Hoshino

PLOS ONE, 2026, vol. 21, issue 3, 1-23

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.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0344167 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 44167&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0344167

DOI: 10.1371/journal.pone.0344167

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().