Seasonal Performance Analysis and Comparative Evaluation of Wind Power Prediction Models Using Machine Learning Techniques

This research paper presents a novel approach to wind power prediction, focusing on seasonal analysis and machine learning models. The study addresses short-term wind power forecasting, specifically targeting the prediction of wind power generation at a given location over periods ranging from a few minutes to several days in advance. The proposed methodology integrates comprehensive seasonal analysis, leveraging four distinct seasons namely Winter, Spring, Summer, and Autumn to gain insights into wind energy production patterns. This study evaluates the performance of two machine learning models, kNN Regression and AdaBoost, across these seasons, providing valuable insights into their effectiveness in wind power prediction. This research contributes to advancing wind power forecasting methodologies by offering a comprehensive analysis of seasonal variations and leveraging machine learning techniques for accurate and reliable predictions.