Development of EV charging templates: An improved K-prototypes method

Juhua Hong, Yue Xiang, Youbo Liu, Junyong Liu, Ran Li, Furong Li, Jing Gou

Research output: Contribution to journalArticlepeer-review

9 Citations (SciVal)


© The Institution of Engineering and Technology 2018. In order to manage the charging behaviour of electric vehicles (EVs), this study for the first time develops a set of EV charging load profiles: EV templates. EV charging profiles have unique waveforms similar to a rectangular pulse train. This characteristics significantly limits the performance of clustering analysis in that traditional distance calculation, such as Euclidean distance, which cannot reflect the morphological dissimilarities. This study proposes a novel clustering method using rough set theory to accurately measure the dissimilarity between the EV profiles. The pulse train waves are firstly extracted as mixed data features, which are partitioned by an improved K-prototypes method based on rough set distance. The proposed method is implemented on the real charging load profiles and compared with K-means and traditional K-prototypes. Their clustering performances are evaluated by diverse validity indices. The results show that the proposed method outperforms other comparison methods.
Original languageEnglish
Pages (from-to)4361-4367
Number of pages7
JournalIET Generation, Transmission and Distribution
Issue number20
Publication statusPublished - 13 Nov 2018


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