Predicting electric vehicle (EV) charging events is crucial for load scheduling and energy management, promoting seamless transportation electrification and decarbonization. While prior studies have focused on EV charging demand prediction, primarily for public charging stations using historical charging data, home charging prediction is equally essential. However, existing prediction methods may not be suitable due to the unavailability of or limited access to home charging data. To address this research gap, inspired by the concept of non-intrusive load monitoring (NILM), we develop a home charging prediction method using historical smart meter data. Different from NILM detecting EV charging that has already occurred, our method provides predictive information of future EV charging occurrences, thus enhancing its utility for charging management. Specifically, our method, leverages a self-attention mechanism-based transformer model, employing a ``divide-conquer'' strategy, to process historical meter data to effectively and learn EV charging representation for charging occurrence prediction. Our method enables prediction at one-minute interval hour-ahead. Experimental results demonstrate the effectiveness of our method, achieving consistently high accuracy of over 96.81\% across different prediction time spans. Notably, our method achieves high prediction performance solely using smart meter data, making it a practical and suitable solution for grid operators.

翻译：预测电动汽车充电事件对于负荷调度和能源管理至关重要，有助于推动交通电气化和脱碳进程。以往研究主要关注电动汽车充电需求预测，尤其是针对公共充电站利用历史充电数据进行预测，然而家庭充电预测同样重要。但由于家庭充电数据难以获取或受限，现有预测方法可能并不适用。为填补这一研究空白，受非侵入式负荷监测（NILM）概念的启发，本文提出了一种利用历史智能电表数据的家庭充电预测方法。与检测已发生电动汽车充电的NILM不同，本方法提供未来电动汽车充电事件的预测信息，从而增强其在充电管理中的实用性。具体而言，本方法采用基于自注意力机制的Transformer模型，运用"分而治之"策略处理历史电表数据，有效学习电动汽车充电表征以预测充电事件。该方法能够实现提前一小时、每分钟间隔的预测。实验结果表明，本方法在不同预测时间跨度下均保持超过96.81%的高准确率。值得注意的是，本方法仅依靠智能电表数据即可实现高精度预测，为电网运营商提供了实用且合适的解决方案。