Wind Speed Prediction by Hybridization of Classification and Prediction Model Perspective on Machine Learning Algorithms

This paper introduces a novel hybrid machine learning model for predicting wind speed, aimed at enhancing the efficiency and accuracy of wind energy prediction. Traditional methods often struggle with the variability and sudden change nature of wind patterns. To address this, our model harmoniously integrates classification and regression techniques, using the Catboost, Adaboost, Gradientboosting, and Random Forest algorithms. By categorizing wind speeds into three velocity levels before predictive modeling, the approach significantly refines the accuracy of forecasts. This methodology gives a way for tailored algorithmic responses to varying wind conditions, thereby possibly improving the operational reliability and maintenance scheduling of wind turbines. Comparative results demonstrate that our hybrid model surpasses conventional approaches in most cases for both root mean square error (RMSE) and mean absolute error (MAE) metrics, particularly in handling abrupt wind speed fluctuations. Also, the proposed model not only advances the predictive capabilities of wind speed forecasts but also can contribute to the sustainable development of wind energy resources. This study lays the foundation for future research into real-time adaptive wind speed prediction systems.