发表机构
Tsinghua Shenzhen International Graduate School, Tsinghua University; Intelligent Transportation Thrust, The Hong Kong University of Science and Technology (Guangzhou)（清华大学深圳国际研究生院; 香港科技大学（广州）智能交通学域）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对电动汽车充电负荷的行为异质性与时间变异性挑战，提出行为引导型在线概率预测框架，经10个真实充电站实验，其在1小时、4小时预测中均显著优于基线方法，实现稳定性能提升。
AI 中文摘要
电动汽车（EV）充电负荷呈现出强烈的行为异质性和时间变异性，在工况不断演变的情况下，给在线概率预测带来了重大挑战。具体而言，不同充电站的持续充电模式可能存在显著差异，而近期的行为变化会持续改变潜在的负荷分布。本文提出一种行为引导型在线概率预测框架，该框架明确刻画了特定充电站的持续模式与近期行为变化，构建了双时间尺度行为表示，以区分长期充电特征与近期行为状态并量化二者偏差；进一步将这些行为变化进行语义编码，以指导感知漂移的预测适配，同时采用延迟反馈机制，确保在不同预测 horizon 观测值可用时实现时间一致的在线更新。对10个异质性真实充电站的实验表明，所提方法在预测精度和概率可靠性上始终优于传统预测模型及感知概念漂移的在线基线方法：对于提前1小时的预测，所提方法相比对应最优基线分别降低了15.3%的均方误差（MSE）和17.8%的分位数损失（Pinball loss）；对于提前4小时的预测，提升幅度进一步达到16.8%和22.6%，证明其在充电行为演变及更长预测 horizon 下均能实现稳定的性能提升。
英文摘要
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.
CommentsSubmitted to IEEE
