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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 du
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T20:20:41.000Z
First collected: 2026-09-20T08:20:57.646Z. This is not the publication date.