Combining Statistical and Machine Learning Methodologies in Energy Consumption Forecasting for Electric Vehicles
TL;DR
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, ensuring that models dynamically adapt to real-world scenarios.
Abstract
Achieving the Sustainable Development Goals (SDGs) requires a transition from conventional fossil-fuel-powered vehicles to alternative energy sources, such as electricity. However, reliably predicting energy consumption under highly variable real-world driving conditions and across different temporal horizons remains a critical challenge regarding the widespread adoption of Electric Vehicles (EVs), directly impacting the precision of range estimation, 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 that 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.