Forecasting Session Characteristics for Electric Vehicle Charging StationsPavšič, Amadej (Avtor) Demšar, Jure (Mentor) electric vehicle chargingdeep learningtransformerstime seriesenergy management systemtransfer learningThe rapid adoption of electric vehicles (EVs) strains existing power grids, creating a need for intelligent Energy Management Systems (EMS) driven by accurate forecasting. This thesis addresses the stochastic nature of EV charging by systematically evaluating three forecasting paradigms – aggregated grid occupancy, individual session parameters at plug-in, and generative session sequencing. Utilizing real-world datasets from workplace charging environments and applying transfer learning strategies, we evaluate a suite of models ranging from classical machine learning to state-of-the-art transformer architectures. The results demonstrate that aggregated forecasting using fine-tuned transformers achieves high accuracy and is viable for immediate deployment in EMS. Conversely, session-level prediction remains difficult due to feature scarcity and behavioral variance. However, the proposed autoregressive sequence model successfully captures macro-level charging patterns, proving its value for stochastic grid simulation despite limited deterministic accuracy.20252025-12-17 14:40:09Magistrsko delo/naloga177209sl