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arXiv AI By Chenghan Li, Qingxiang Liu, Yinliang Xu, Yuxuan Liang

A Behavior-Guided Online Probabilistic Forecasting Method for Electric vehicle Charging Loads

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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