Machine Learning-Based Active Wind Power Pre-diction Using Comparative Regression Models
Authors-Sai Bhanu Prasad, Pallavi Malloju
Keyword-Active Wind Power Prediction, Renewable Energy, Machine Learning, Linear Regression, Ridge Regression, Lasso Regression, K-Neighbors Regression, Decision Tree, Gradient Boost-ing, Principal Component Analysis, Wind Turbine, Energy Forecasting.
The rapid expansion of renewable energy technologies has increased the need for accurate fore-casting techniques that can effectively estimate power generation from wind energy systems. Because wind conditions fluctuate continuously due to changing atmospheric and environmental factors, predicting the amount of electrical power generated by wind turbines remains a challeng-ing task. Reliable forecasting models are essential for improving grid stability, optimizing energy distribution, minimizing operational uncertainty, and supporting efficient utilization of renewable energy resources. In this research, a estimate active wind power by comparing the performance of multiple regression algorithms. The study employs six widely used supervised learning mod-els, namely Linear Regression, Ridge Regression, Lasso Regression, K-Neighbors Regression, Decision Tree Regression, and Gradient Boosting Regression. A real-world wind turbine dataset obtained from the Kaggle repository, consisting of meteorological measurements and turbine operational parameters recorded at regular intervals, is used for model development and evalua-tion. Before training the models, the dataset undergoes comprehensive preprocessing, including missing value estimation through imputation, detection and treatment of abnormal observations, feature correlation analysis, K-Neighbors Regression algorithm consistently delivers superior prediction accuracy compared with the remaining regression techniques, producing the lowest prediction errors and the highest coefficient of determination. The outcomes of this study con-firm that machine learning-based regression methods provide an effective solution for active wind power forecasting and can significantly contribute to intelligent energy management, im-proved scheduling of renewable power generation, and the reliable operation of modern smart grid systems.
Doi-[https://doi.org/10.5281/zenodo.21678954]