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

2026-08-25

AI Summary

本文提出了一种行为引导的在线概率预测方法，通过区分长期充电特性和近期行为状态来解决电动汽车充电负荷预测问题，提高了预测精度。

Abstract

Electric vehicle (EV) charging loads exhibit strong behavioral heterogeneity and temporal variability, posing significant challenges for online probabilistic forecasting under evolving operating conditions. In particular, persistent charging patterns may differ substantially across stations, while recent behavioral changes can continuously alter the underlying load distributions. This paper proposes a behavior-guided online probabilistic forecasting framework that explicitly characterizes persistent station-specific patterns and recent behavioral changes. A dual-timescale behavior representation is constructed to distinguish long-term charging characteristics from recent behavioral states and quantify their deviations. These behavioral changes are further semantically encoded to guide drift-aware forecasting adaptation, while a delayed-feedback mechanism ensures temporally consistent online updates when observations become available across different forecasting horizons. Experiments on ten heterogeneous real-world charging stations demonstrate that the proposed method consistently outperforms conventional forecasting models and concept-drift-aware online baselines in forecasting accuracy and probabilistic reliability. For 1-h-ahead forecasting, the proposed method reduces MSE and Pinball loss by 15.3\% and 17.8\%, respectively, over the corresponding best baselines. For 4-h-ahead forecasting, the improvements further reach 16.8\% and 22.6\%, respectively, demonstrating consistent performance gains under evolving charging behaviors and extended forecasting horizons.

Problem

• Electric vehicle charging loads
• behavioral heterogeneity
• temporal variability
• online probabilistic forecasting
• operating conditions

Innovation

• behavior-guided
• dual-timescale behavior representation
• drift-aware forecasting
• delayed-feedback mechanism