Machine Learning Models for Predicting and Managing Electric Vehicle Load in Smart Grids

Abstract

The integration of electric vehicles (EVs) into smart grids provides major issues and prospects for effective energy management. This research examines the actual utilization of machine learning models to forecast and manage EV demand in smart grids, intended to increase grid effectiveness and dependable operation. We acquire and preprocess different datasets, considering elements such as time of usage, characteristics of the environment, and user behaviors. Multiple machine learning models, combining neural networks, support vector machines, and forests that are random, are developed and rated for their projected accuracy. Our results imply that enhanced prediction algorithms may considerably raise all the level of detail of EV load forecasts. Furthermore, we recommend load management systems based on real-time forecasts to enhance energy distribution and lower peak demand. This study presents a potential of machine learning that would promote the integration of EVs into smart grids, that tie in to more capable and efficient energy systems.