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Machine learning-based wind speed prediction using random forest: a cross-validated analysis for renewable energy applications

Year 2025, Volume: 9 Issue: 3, 508 - 518

Ahmet Durap

https://doi.org/10.31127/tuje.1624354

Abstract

Wind speed prediction plays a crucial role in renewable energy planning and optimization. This study presents a comprehensive analysis of wind speed forecasting using Random Forest (RF) models. The research utilized high-resolution wind speed data collected throughout 2023 at the Bowen Abbot facility. Our methodology employed a RF with cross-validation techniques to ensure model stability and reliability. The model demonstrated robust performance across multiple evaluation metrics, achieving an average R² score of 0.9155 (±0.0035) through 5-fold cross-validation. Error analysis revealed consistent performance across training, testing, and validation sets, with root mean square errors (RMSE) of 0.6624 (±0.0098) m/s. Feature importance analysis revealed that the 3-hour rolling mean wind speed was the most influential predictor, accounting for 89.84% of the model's predictive power, followed by 1-hour (2.59%) and 3-hour (2.57%) lagged wind speeds. This hierarchical importance of temporal features suggests that recent wind patterns are crucial for accurate predictions. The error distribution analysis showed approximately normal distributions with slight deviations in the tails, particularly in the validation set (kurtosis: 5.2146). Key findings indicate that the model maintains high prediction accuracy across different temporal scales, with mean absolute errors (MAE) averaging 0.4998 (±0.0098) m/s. The model's stability across different data partitions suggests its reliability for operational deployment. These results demonstrate the potential of RF algorithms for accurate wind speed forecasting in renewable energy applications, providing a valuable tool for wind power generation planning and management. The study's findings contribute to the growing body of research on machine learning applications in renewable energy, offering insights into model performance evaluation and error analysis methodologies for wind speed prediction systems.

Keywords

wind speed prediction cross-validated analysis renewable energy applications feature engineering performance metrics

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There are 42 citations in total.

Details

Primary Language English
Subjects Coastal Sciences and Engineering
Journal Section Articles
Authors

Ahmet Durap 0000-0002-6218-0129

Early Pub Date March 8, 2025
Publication Date
Submission Date January 21, 2025
Acceptance Date March 6, 2025
Published in Issue Year 2025 Volume: 9 Issue: 3

Cite

APA Durap, A. (2025). Machine learning-based wind speed prediction using random forest: a cross-validated analysis for renewable energy applications. Turkish Journal of Engineering, 9(3), 508-518. https://doi.org/10.31127/tuje.1624354
AMA Durap A. Machine learning-based wind speed prediction using random forest: a cross-validated analysis for renewable energy applications. TUJE. March 2025;9(3):508-518. doi:10.31127/tuje.1624354
Chicago Durap, Ahmet. “Machine Learning-Based Wind Speed Prediction Using Random Forest: A Cross-Validated Analysis for Renewable Energy Applications”. Turkish Journal of Engineering 9, no. 3 (March 2025): 508-18. https://doi.org/10.31127/tuje.1624354.
EndNote Durap A (March 1, 2025) Machine learning-based wind speed prediction using random forest: a cross-validated analysis for renewable energy applications. Turkish Journal of Engineering 9 3 508–518.
IEEE A. Durap, “Machine learning-based wind speed prediction using random forest: a cross-validated analysis for renewable energy applications”, TUJE, vol. 9, no. 3, pp. 508–518, 2025, doi: 10.31127/tuje.1624354.
ISNAD Durap, Ahmet. “Machine Learning-Based Wind Speed Prediction Using Random Forest: A Cross-Validated Analysis for Renewable Energy Applications”. Turkish Journal of Engineering 9/3 (March 2025), 508-518. https://doi.org/10.31127/tuje.1624354.
JAMA Durap A. Machine learning-based wind speed prediction using random forest: a cross-validated analysis for renewable energy applications. TUJE. 2025;9:508–518.
MLA Durap, Ahmet. “Machine Learning-Based Wind Speed Prediction Using Random Forest: A Cross-Validated Analysis for Renewable Energy Applications”. Turkish Journal of Engineering, vol. 9, no. 3, 2025, pp. 508-1, doi:10.31127/tuje.1624354.
Vancouver Durap A. Machine learning-based wind speed prediction using random forest: a cross-validated analysis for renewable energy applications. TUJE. 2025;9(3):508-1.
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