AUTHOR=Hu Jingwei , Ren Rufei , Hu Jie , Sun Qiuye 

TITLE=Nonintrusive Monitoring for Electric Vehicles Based on Zero-Shot Learning

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 9 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2021.720391

DOI=10.3389/fenrg.2021.720391

ISSN=2296-598X

ABSTRACT=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.