EVSense: a robust and scalable approach to non-intrusive EV charging detection

Xudong Wang, Guoming Tang, Yi Wang, Srinivasan Keshav, Yu Zhang

Abstract: As the number of electric vehicles (EVs) increases, large-scale residential EV charging will burden the power grid, posing problems for both planning and operations. Promptly capturing EV charging events can help mitigate this problem. However, most existing grid operators lack dedicated sensors for residential EV monitoring. This motivates non-intrusive load monitoring (NILM) as a technique to gain fine-grain EV charging information. We present EVSense, a robust deep neural network (DNN) based model for non-intrusive EV charging detection. We also show how to use federated transfer learning (FTL) to deploy our system on resource-constrained edge devices. This makes EVSense a feasible solution for large-scale EV monitoring. We evaluate EVSense on both real-world and synthetic datasets and find that it can achieve higher precision and more robust charging detection compared to existing learning-based and rule-based approaches. We also find that, due to the use of FTL, EVSense also shows excellent scalability despite diversity in residential load profiles, sampling rates, and seasons.