Smart Energy Allocation for Electric Vehicles Using Predictive Modelling

Efficient energy allocation for electric vehicle (EV) charging is critical to optimize grid performance and enhance user satisfaction. In this research, we utilize historical EV charging data from urban networks to develop predictive models for smart energy allocation. By applying advanced machine learning algorithms, we establish correlations between past charging patterns and real-time demand, enabling accurate short-term energy demand forecasting. Performance metrics such as Root Mean Squared Error (RMSE) and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Score are employed to evaluate model accuracy. Our findings reveal that Gradient Boosting techniques achieve superior accuracy, minimizing forecasting errors and ensuring balanced energy distribution. This approach supports both grid operators and EV users by enhancing charging efficiency and reducing energy waste.