{"id":10181,"date":"2026-08-17T21:28:28","date_gmt":"2026-08-17T13:28:28","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=10181"},"modified":"2026-08-17T23:59:38","modified_gmt":"2026-08-17T15:59:38","slug":"jase-202611-34-050","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202611-34-050","title":{"rendered":"Prediction of Wind Power Using an Optimized Ensemble Machine Learning Model"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 34<\/a><\/p>\n<\/div>\n\n\n\n

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Arangarajan Vinayagam1<\/sup><\/i><\/a>, Kavitha M V2<\/sup>, Deepa A3<\/sup>, Senthil Kumar H4<\/sup>, Arivoli Sundaramurthy5<\/sup>, and Saravanan K6<\/sup><\/p>\n\n\n\n

1<\/sup>Department of Electrical and Electronics Engineering, New Horizon College of Engineering, Bangalore, India<\/p>\n\n\n\n

2<\/sup>Department of Electronics and Communication Engineering, Cambridge Institute of Technology, Bangalore, India<\/p>\n\n\n\n

3<\/sup>Department of Electronics and Communication Engineering, Gopalan College of Engineering and Management, Bangalore, India<\/p>\n\n\n\n

4<\/sup>Department of Computer Science and Engineering, Presidency University, Bangalore, India<\/p>\n\n\n\n

5<\/sup>Department of Electrical and Electronics Engineering, PSG Institute of Technology and Applied Research, Coimbatore, India<\/p>\n\n\n\n

6<\/sup>Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur-603203,
Chengalpattu, Tamil Nadu, India<\/p>\n<\/div>\n\n\n\n

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Received: May 07, 2026
Accepted: June 02, 2026
Publication Date: August 17, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Process Steps of GS-CV Strategy <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202611_34.050<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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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<\/sup> 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.<\/p>\n\n\n\n

Keywords: Renewable Energy, Machine Learning, Gradient Boosting, Extreme Gradient Boosting, Random Forest, Wind Power Prediction<\/em><\/p>\n\n\n\n

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  1. [1] E. A. Tuncar, \u015e. Sa\u011flam, and B. Oral, (2024) \u201cA Review of Short-Term Wind Power Generation Forecasting Methods in Recent Technological Trends<\/i>\u201d Energy Reports 12<\/b>: 197\u2013209. DOI: 10.1016\/j.egyr.2024.01.12.3.<\/li>\n
  2. [2] H. R. Alsamamra, S. Salah, and J. H. Shoqeir, (2024) \u201cPerformance Analysis of ARIMA Model for Wind Speed Forecasting in Jerusalem, Palestine<\/i>\u201d Energy Exploration & Exploitation 42<\/b>(5): 1727\u20131746. DOI: 10.1177\/01445987241234567.<\/li>\n
  3. [3] A. Alkesaiberi, F. Harrou, and Y. Sun, (2022) \u201cEfficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study<\/i>\u201d Energies 15<\/b>(7): 2327. DOI: 10.3390\/en15072327.<\/li>\n
  4. [4] Z.-Y. Fu, G.-Q. Li, W.-D. Tang, Z.-H. Pan, and W.-C. Zhang, (2025) \u201cNovel Nonlinear Wind Power Prediction Based on Improved Iterative Algorithm<\/i>\u201d Systems Science & Control Engineering 13<\/b>(1): 2448626. DOI: 10.1080\/21642583.2025.2448626.<\/li>\n
  5. [5] Z. Ti, X. W. Deng, and M. Zhang, (2021) \u201cArtificial Neural Networks Based Wake Model for Power Prediction of Wind Farm<\/i>\u201d Renewable Energy 172<\/b>: 618\u2013631. DOI: 10.1016\/j.renene.2021.03.056.<\/li>\n
  6. [6] D. A. Savio, V. A. Juliet, B. Chokkalingam, S. Padmanaban, J. B. Holm-Nielsen, and F. Blaabjerg, (2019) \u201cPhotovoltaic Integrated Hybrid Microgrid Structured Electric Vehicle Charging Station and Its Energy Management Approach<\/i>\u201d Energies 12<\/b>(1): 168. DOI: 10.3390\/en12010168.<\/li>\n
  7. [7] J. Ferrero Bermejo, J. F. Gomez Fernandez, F. Olivencia Polo, and A. Crespo M\u00e1rquez, (2019) \u201cA Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction: Solar PV, Hydraulic and Wind Energy Sources<\/i>\u201d Applied Sciences 9<\/b>(9): 1844. DOI: 10.3390\/app9091844.<\/li>\n
  8. [8] Z. Liu, H. Guo, Y. Zhang, and Z. Zuo, (2025) \u201cA Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges<\/i>\u201d Energies 18<\/b>(2): 350. DOI: 10.3390\/en18020350.<\/li>\n
  9. [9] Z. Mustaffa and M. H. Sulaiman, (2025) \u201cRandom Forest Based Wind Power Prediction Method for Sustainable Energy System<\/i>\u201d Cleaner Energy Systems 12<\/b>: 100210. DOI: 10.1016\/j.cles.2025.100210.<\/li>\n
  10. [10] T. A. Rajaperumal and C. C. Columbus, (2025) \u201cEnhanced Wind Power Forecasting Using Machine Learning, Deep Learning Models and Ensemble Integration<\/i>\u201d Scientific Reports 15<\/b>(1): 20572. DOI: 10.1038\/s41598-025-20572-3.<\/li>\n
  11. [11] R. A. Sobolewski, M. Tchakorom, and R. Couturier, (2023) \u201cGradient boosting-based approach for short-and medium-term wind turbine output power prediction<\/i>\u201d Renewable Energy 203<\/b>: 142\u2013160. DOI: 10.1016\/j.renene.2022.12.040.<\/li>\n
  12. [12] S. Ponrekha A, M. S. P. Subathra, C. Baratiraja, and N. Manoj Kumar, (2025) \u201cA Topology Review and Comparative Analysis on Transformerless Grid-Connected Photovoltaic Inverters and Leakage Current Reduction Techniques<\/i>\u201d IET Renewable Power Generation 19<\/b>(1): e12655. DOI: 10.1049\/rpg2.12655.<\/li>\n
  13. [13] X. Xiong, X. Guo, P. Zeng, R. Zou, and X. Wang, (2022) \u201cA Short-Term Wind Power Forecast Method via XGBoost Hyper-Parameters Optimization<\/i>\u201d Frontiers in Energy Research 10<\/b>: 905155. DOI: 10.3389\/fenrg.2022.905155.<\/li>\n
  14. [14] S. Guan, Y. Wang, L. Liu, J. Gao, Z. Xu, and S. Kan, (2023) \u201cUltra-Short-Term Wind Power Prediction Method Combining Financial Technology Feature Engineering and XGBoost Algorithm<\/i>\u201d Heliyon 9<\/b>(6): e16938. DOI: 10.1016\/j.heliyon.2023.e16938.<\/li>\n
  15. [15] Xiangcheng, J. Wang, Z. Geng, Y. Jin, and J. Xu, (2023) \u201cShort-Term Wind Power Prediction Method Based on Genetic Algorithm Optimized XGBoost Regression Model<\/i>\u201d Journal of Physics: Conference Series 2527<\/b>(1): 012061. DOI: 10.1088\/1742-6596\/2527\/1\/012061.<\/li>\n
  16. [16] N. T. Tran, T. T. G. Tran, T. A. Nguyen, and M. B. Lam, (2023) \u201cA new grid search algorithm based on XGBoost model for load forecasting<\/i>\u201d Bulletin of Electrical Engineering and Informatics 12<\/b>(4): 1857\u20131866. DOI: 10.11591\/eei.v12i4.4907.<\/li>\n
  17. [17] B. \u0130sen. Wind Turbine SCADA Dataset<\/i>. Accessed: February 13, 2026. 2018.<\/li>\n
  18. [18] C. El-Morr, M. Jammal, H. Ali-Hassan, and W. El-Hallak, (2022) \u201cMachine Learning for Practical Decision Making<\/i>\u201d International Series in Operations Research and Management Science<\/b>: DOI: https:\/\/doi.org\/10.1007\/978-3-031-16990-8<\/a>.<\/li>\n
  19. [19] U. Singh, M. Rizwan, M. Alaraj, and I. Alsaidan, (2021) \u201cA Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step Towards Smart Grid Environments<\/i>\u201d Energies 14<\/b>(16): 5196. DOI: 10.3390\/en14165196.<\/li>\n
  20. [20] A. Bilgili and K. G\u00fcl, (2024) \u201cForecasting power generation of wind turbine with real-time data using machine learning algorithms<\/i>\u201d Clean Technologies and Recycling 4<\/b>: 108\u2013124. DOI: 10.3934\/ctr.2024006.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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