Optimizing Electric Vehicle Charging Infrastructure through Hybrid Machine Learning Techniques for Smart Energy Management

As the world begins to move more and more towards Electric Vehicles (EVs), the imperative for innovative solutions to streamline energy management within charging infrastructure intensifies. This study delves into the realm of machine learning integration, focusing particularly on Random Forest (RF) techniques to revolutionize energy optimization in EV charging systems. While Gradient Boosting Machine (GBM) initially garners attention for its adeptness with intricate datasets, RF emerges as a potent complementary approach uniquely suited to handle the complexities of nonlinear relationships. By synergizing the strengths of RF and GBM algorithms, this research endeavors to dynamically refine charging schedules, curtail costs, and fortify grid stability. Through a fusion of historical data and real-time environmental factors, the envisioned "Adaptive Ensemble Learning Framework" (AELF)-driven smart charging infrastructure is primed to recalibrate charging strategies in response to energy demand fluctuations while judiciously balancing user preferences and grid constraints. Rigorous simulations and case studies serve as the litmus test, pitting the efficacy of the AELF approach against the conventional Decision Trees Model and Support Vector Machines Technique. The results tout enhancements of up to 15% across diverse performance metrics, underscoring its prowess in charting the course towards a sustainable and intelligent transportation ecosystem.