A shape-based clustering method for pattern recognition of residential electricity consumption

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

35 Scopus Citations
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  • Lulu Wen
  • Kaile Zhou
  • Shanlin Yang

Related Research Unit(s)


Original languageEnglish
Pages (from-to)475-488
Journal / PublicationJournal of Cleaner Production
Online published7 Dec 2018
Publication statusPublished - 1 Mar 2019


Pattern recognition of residential electricity consumption refers to discover different electricity consumption patterns from electricity consumption data (ECD), which can provide valuable insights for developing personalized marketing strategies, supporting targeted demand side management, and improving energy utilization efficiency. To improve the efficiency and effectiveness of ECD analysis, we proposed an improved K-means algorithm, in which principal component analysis (PCA) was used to reduce the dimensions of smart meter time series data and the initial cluster centers were optimized. 3000 daily electricity consumption profiles (ECPs) of 1000 residents, obtained from the smart metering electricity customer behavior trials of Irish, and 2000 yearly residential ECPs from Jiangsu Province, China, were used in the experiments. The ECPs were divided into 7 and 4 clusters respectively based on their ECPs, and the characteristics of each cluster were extracted. In addition, the changes of residential electricity consumption are also reflected in the shape variation of ECPs. However, traditional similarity measurements cannot find the shape similarity of ECPs. Therefore, a shape-based clustering method was also proposed to group ECPs with similar shapes and the detailed algorithm procedures were provided. The results showed that the shape-based clustering method can effectively find similar shapes and identify typical electricity consumption patterns based on daily ECPs.

Research Area(s)

  • Dynamic time wrapping, Electricity consumption pattern, Shape-based clustering, Smart meter data

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).