XGBoost Session-Level Energy Demand Prediction for EV Charging Stations Using Behavioral and Contextual Data

Abstract

This paper proposes a data-driven approach to predict session-level EV energy demand using contextual station data and user behavior from historical logs. A large realistic dataset and a time-aware, user-level anti-leakage split ensured robust evaluation. The XGBoost model outperformed other regressors, offering accurate forecasts without vehicle telemetry. This scalable method enables operators to predict energy demand reliably, supporting smarter EV charging network management and allowing future integration of detailed user or technical data.