Sparse-coding-based household clustering for demand response services

Shintaro Ikeda, Hiroaki Nishi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


For the development of a smart grid, smart meter is a key device to measure the electric power usage of network-connected houses. Smart meters are currently being installed into households, and play the important role for providing a demand response service. A demand response is a necessary service in order to adjust supply-demand balancing because the balance is kept by the cost in the electricity market. Therefore, the customers of the electricity supply companies are expected to be optimally assigned as a group. In this paper, we separate a set of households into clusters as optimally assigned customers. When conducting the clustering, the utilization of unsupervised learning using data from a smart meter is required. In this study, we propose a method of household clustering for a demand response event by sparse coding, which is a type of neural network. The proposed method generates a power consumption model of each household, finds simple relationship distances between households, and conducts hierarchical clustering based on these distances. In addition, to extract the characteristics of fluctuating load usage, we conduct data normalization that cuts off at a fixed load usage of each household. To confirm the effect of the proposed clustering method, the usage tendency of household air conditioning (A/C) units was evaluated.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE 25th International Symposium on Industrial Electronics, ISIE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509008735
Publication statusPublished - 2016 Nov 15
Event25th IEEE International Symposium on Industrial Electronics, ISIE 2016 - Santa Clara, United States
Duration: 2016 Jun 82016 Jun 10


Other25th IEEE International Symposium on Industrial Electronics, ISIE 2016
Country/TerritoryUnited States
CitySanta Clara


  • Demand response
  • Household clustering
  • Smart meter
  • Sparse coding

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering


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