Clustering time-series energy data from smart meters

Alexander Lavin*, Diego Klabjan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


Investigations have been performed into using clustering methods in data mining time-series data from smart meters. The problem is to identify patterns and trends in energy usage profiles of commercial and industrial customers over 24-h periods and group similar profiles. We tested our method on energy usage data provided by several U.S. power utilities. The results show accurate grouping of accounts similar in their energy usage patterns, and potential for the method to be utilized in energy efficiency programs.

Original languageEnglish (US)
Pages (from-to)681-689
Number of pages9
JournalEnergy Efficiency
Issue number4
StatePublished - Jul 25 2015


  • Electricity load profile
  • Energy efficiency program
  • Smart meter
  • Time-series clustering
  • k-means

ASJC Scopus subject areas

  • Energy(all)


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