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.

预测电动汽车（EV）充电事件对于负载调度和能源管理至关重要，本研究使用历史智能电表数据开发了一种家庭充电预测方法，借鉴非侵入式负载监测（NILM）的概念，通过基于自注意力机制的变压器模型，提供了未来 EV 充电事件的预测信息，本方法在一分钟间隔提供高达 96.81％的准确性，适用于网格运营商的实际需求。