Synthetic Electric Vehicle Charging Session Generation Using a Conditional Variational Autoencoder
The increasing adoption of electric vehicles (EVs) is expected to place significant additional demand on residential distribution networks, creating a need for realistic charging datasets for planning and simulation studies. However, access to real-world EV charging data is often limited due to privacy constraints, incomplete records, and restricted availability. This paper proposes a conditional variational autoencoder (CVAE) for the generation of synthetic EV charging sessions from real transaction-level charging data. The model is trained on engineered session features describing plug-in duration, charging duration, delivered energy, charging delay, and cyclical time-of-week, while con...
| Authors | Graeme Kelly, Emilio J. Palacios-Garcia, Barry P. Hayes |
|---|---|
| Primary category | eess.SY |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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