Nonintrusive Monitoring for Electric Vehicles Based on Zero-Shot Learning - Hu, Jingwei; Ren, Rufei; Hu, Jie; Sun, Qiuye (Frontiers in Energy Research, 2021)
Monitoring the charging behavior of electric vehicle (EV) clusters is helpful in developing more effective energy management strategies for grid operators. Non-intrusive monitoring for EVs has a comprehensive application prospect due to its low implementation cost. Aiming at the problem that traditional non-intrusive monitoring methods cannot identify unknown devices accurately due to the lack of classes, a non-intrusive monitoring method based on zero-shot learning (ZSL) is proposed in this paper, which can monitor the unknown types of EVs connected to charging piles. Firstly, the charging characteristics of known EVs and unknown EVs are extracted by dictionary learning. Then EVs is classified by ZSL based on sparse coding. Furthermore, EVs is decomposed based on the proposed multimode factorial hidden markov model (FHMM). Finally, the EVs dataset of Pecan Street is used to verify effectiveness and accuracy of the proposed method.