Abstract
Monitoring the charging behavior of electric vehicle clusters will contribute to developing more effective energy management strategies for grid operators. A low implementation cost leads to a wide application prospect in nonintrusive monitoring for EVs. Aiming at the problem that traditional nonintrusive monitoring methods cannot identify unknown devices accurately due to the lack of classes, a nonintrusive monitoring method based on zero-shot learning (ZSL) is proposed in this article, one which can monitor the unknown types of EVs connected to charging piles. First, the charging characteristics of known EVs and unknown EVs are extracted by dictionary learning. Then EVs are classified by ZSL based on sparse coding. Furthermore, EVs are decomposed based on the proposed multimode factorial hidden Markov model (FHMM). Finally, the EV dataset of Pecan Street is used to verify the effectiveness and accuracy of the proposed method.
Highlights
With the continuous improvement of the penetration of renewable energy, the gradual decline in the electricity price has been making EVs more appealing to consumers (Liu et al, 2013)
This study proposes a nonintrusive EV monitoring method based on the zeroshot learning (ZSL) factor hidden Markov model
The charge–discharge characteristics of known EVs and unknown EVs connected to the charging pile were extracted by dictionary learning
Summary
With the continuous improvement of the penetration of renewable energy, the gradual decline in the electricity price has been making EVs more appealing to consumers (Liu et al, 2013). On the basis of NIM, a training-free, nonintrusive load extraction algorithm was proposed based on boundary box fitting and load characteristics (Zhao et al, 2019), which can automatically identify the start time, end time, and power amplitude of charging events. A nonintrusive identification method for EV charging curve extraction driven by a depth generation model is proposed in the study by Wang et al (2020b). This study proposes a nonintrusive EV monitoring method based on the ZSL factor hidden Markov model.
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