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The paper introduces a behavior-guided online probabilistic forecasting framework for electric vehicle charging loads that captures both persistent station-specific patterns and recent behavioral changes through a dual-timescale representation. It employs semantic encoding of behavioral shifts to adapt forecasts in a drift-aware manner and uses a delayed-feedback mechanism to maintain temporal consistency across horizons. Experiments on ten real-world charging stations show consistent improvements over conventional models, reducing MSE and Pinball loss by up to 22.6% for 4‑hour ahead forecasts.
arXiv:2606. 19199v1 Announce Type: cross Abstract: The recent growth of EV adoption poses challenges for power systems, including increased peak demand and potential grid instability.
arXiv:2607. 09400v1 Announce Type: cross Abstract: Adaptive power management in Electric Vehicles (EVs) requires accurate power prediction.
The paper introduces a conditional variational autoencoder (CVAE) to generate synthetic electric vehicle (EV) charging sessions from real transaction-level data. It trains on engineered features such as plug‑in duration, charging duration, delivered energy, charging delay, and cyclical time‑of‑week, conditioning on day of week and managed charging status. Evaluation shows the synthetic data preserves key statistical properties and supports predictive modelling tasks via a Train‑on‑Synthetic‑Test‑on‑Real protocol.
arXiv:2511. 09789v3 Announce Type: replace Abstract: Short-term energy forecasting plays an important role in real-time operational decision-making, such as electricity market bidding and power system dispatch, where both numerical accuracy and correct directional signals are essential.
Reliable electric vehicle (EV) charging infrastructure is a cornerstone of sustainable, low-carbon cities, yet urban climate stress such as extreme heat, heavy precipitation, and humidity increasingly raises equipment fault risk and undermines the resilience of urban energy and mobility services. Shifting operation from reactive repair to preventive maintenance depends on accurate, forward-looking fault-risk prediction, a task complicated by the heterogeneous time scales of physical, behavioral, contextual, and historical signals and by forecasting over a multi-week horizon.