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Identifying household electricity consumption patterns: A case study of Kunshan, China

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  • Yang, Ting
  • Ren, Minglun
  • Zhou, Kaile

Abstract

A case study of residential electricity consumption patterns mining and abnormal user identification using hierarchical clustering is presented in this paper. First, based on a brief introduction of hierarchical clustering, a process model and the specific steps of electricity consumption patterns mining in smart grid environment are proposed. Then, a case study using the daily electricity consumption data of 300 residential users in an eastern city of China, Kunshan, from November 16, 2014 to December 16, 2014, is presented. Through the implementation of hierarchical clustering, 9 abnormal users and 4 types of monthly electricity consumption patterns are successfully identified. The results show that most residential users in Kunshan city, nearly 81%, have a similar monthly electricity consumption pattern. Their average daily electricity consumption is about 7.73 kWh in the early winter with small fluctuations. Also, their daily electricity consumption is significantly associated with the temperature changes. However, it is worth noting that the special electricity consumption patterns of a small proportion of electricity users cannot be ignored, which is of great significance for the planning, operation, policy formulation and decision-making of smart grid.

Suggested Citation

  • Yang, Ting & Ren, Minglun & Zhou, Kaile, 2018. "Identifying household electricity consumption patterns: A case study of Kunshan, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 861-868.
    • Handle: RePEc:eee:rensus:v:91:y:2018:i:c:p:861-868
    DOI: 10.1016/j.rser.2018.04.037
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    10. Rhodes, Joshua D. & Cole, Wesley J. & Upshaw, Charles R. & Edgar, Thomas F. & Webber, Michael E., 2014. "Clustering analysis of residential electricity demand profiles," Applied Energy, Elsevier, vol. 135(C), pages 461-471.
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