Open Access

ARTICLE

Predicting Short-Term Wind Power Generation at Musalpetti Wind Farm: Model Development and Analysis

Namal Rathnayake¹, Jeevani Jayasinghe^2,3, Rashmi Semasinghe², Upaka Rathnayake^4,*

1 Department of Civil Engineering, Faculty of Engineering, The University of Tokyo, Bunkyo City, Tokyo, 113-8656, Japan
2 Department of Electronics, Faculty of Applied Sciences, Wayamba University of Sri Lanka, Kuliyapitiya, 60200, Sri Lanka
3 Department of Electrical Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia
4 Faculty of Engineering and Design, Atlantic Technological University, Sligo, F91 YW50, Ireland

* Corresponding Author: Upaka Rathnayake. Email:

(This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)

Computer Modeling in Engineering & Sciences 2025, 143(2), 2287-2305. https://doi.org/10.32604/cmes.2025.064464

Received 17 February 2025; Accepted 09 May 2025; Issue published 30 May 2025

Abstract

In this study, a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions. Using data on wind speed, air temperature, nacelle position, and actual power, lagged features were generated to capture temporal dependencies. Among 24 evaluated models, the ensemble bagging approach achieved the best performance, with R² values of 0.89 at 0 min and 0.75 at 60 min. Shapley Additive exPlanations (SHAP) analysis revealed that while wind speed is the primary driver for short-term predictions, air temperature and nacelle position become more influential at longer forecasting horizons. These findings underscore the reliability of short-term predictions and the potential benefits of integrating hybrid AI and probabilistic models for extended forecasts. Our work contributes a robust and explainable framework to support Sri Lanka’s renewable energy transition, and future research will focus on real-time deployment and uncertainty quantification.

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Supplementary Material

Supplementary Material File

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