Performance Comparison of Machine Learning Algorithms for Wind Energy Forecasting in the Coastal Region of Kerala

Abstract

This paper presents the details predicting wind energy output with machine learning models. Accurate forecasts of wind power's future are essential to the feasibility of major renewable energy projects. Making precise forecasts of wind power generation requires accounting for changes in weather patterns over time. This is also essential for issuing early warnings and implementing risk-reduction measures. In this study, prediction models for wind energy are developed using wind data collected at coastal regions in Kerala. Accurate forecasting of wind power generation is necessary to balance supply and demand in the smart grid. In the present investigation, an extensive analysis of long-term wind power forecasting was undertaken utilizing daily wind speed data, employing five distinct machine learning algorithms like XG Boost, LASSO, Gradient Boosting, Random Forest, and Bayesian Ridge Regression.