Napovedovanje značilnosti sej za polnilnice električnih vozil

The 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.