Journal of Applied Science and Engineering

Published by Tamkang University Press

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Prediction of Wind Power Using an Optimized Ensemble Machine Learning Model

 2026

 Volume 34

Arangarajan Vinayagam1, Kavitha M V2, Deepa A3, Senthil Kumar H4, Arivoli Sundaramurthy5, and Saravanan K6

1Department of Electrical and Electronics Engineering, New Horizon College of Engineering, Bangalore, India

2Department of Electronics and Communication Engineering, Cambridge Institute of Technology, Bangalore, India

3Department of Electronics and Communication Engineering, Gopalan College of Engineering and Management, Bangalore, India

4Department of Computer Science and Engineering, Presidency University, Bangalore, India

5Department of Electrical and Electronics Engineering, PSG Institute of Technology and Applied Research, Coimbatore, India

6Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur-603203,
Chengalpattu, Tamil Nadu, India

Received: May 07, 2026
Accepted: June 02, 2026
Publication Date: August 17, 2026

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Process Steps of GS-CV Strategy 

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Wind power forecasting is very crucial to ensure stable renewable energy (RE) integration and the stability of power systems. In this research work, several machine learning (ML) models, such as linear regression (LR), random forest (RF), gradient boosting (GB), extreme GB (XGBoost), and grid search cross-validation (GS-CV) optimized XFGBoost are analyzed in terms of their performance for wind power forecasting using a real-life large dataset having weather and temporal features. Performance of these models is analyzed using metrics such as mean absolute error (MAE), root mean square error (RMSE), and correlation factor (R2). It is found that XGBoost with GS-CV approach consistently performs better than other models with the minimum prediction error and maximum R2 value. The results show that hyperparameter-optimized XGBoost can greatly enhance forecasting accuracy and generalization, providing a practical data-driven solution for wind power forecasting and decision-making.

Keywords: Renewable Energy, Machine Learning, Gradient Boosting, Extreme Gradient Boosting, Random Forest, Wind Power Prediction

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