Combining Statistical and Machine Learning Methodologies in Energy Consumption Forecasting for Electric Vehicles

Source abstract

Achieving the Sustainable Development Goals (SDG) requires a transition from conventional fossil-fuel-powered vehicles to alternative energy sources, such as electricity. However, accurately forecasting energy consumption remains a critical challenge in the widespread adoption of Electric Vehicles (EVs), as it directly impacts operational efficiency, route planning, and charging strategies. To address this, a novel approach is proposed, combining advanced machine learning models—such as XGBoost, Random Forest, and regression-based techniques—with innovative dataset manipulation using statistical methods. The methodology integrates feature engineering to incorporate vehicle-specific metrics, including driving patterns and environmental conditions, ensuring models dynamically adapt to real-world scenarios. The proposed framework demonstrates high accuracy and robustness in predicting energy consumption, providing valuable insights for sustainable transportation and efficient energy management toward SDG achievement.