Design of neuro-fuzzy based intelligent inference algorithm for energy-management system with legacy device

In Hwan Choi, Sung Hyun Yoo, Jun Ho Jung, Myo Taeg Lim, Jung Jun Oh, Moon Kyou Song, Choon Ki Ahn

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Recently, home energy management system (HEMS) for power consumption reduction has been widely used and studied. The HEMS performs electric power consumption control for the indoor electric device connected to the HEMS. However, a traditional HEMS is used for passive control method using some particular power saving devices. Disadvantages with this traditional HEMS is that these power saving devices should be newly installed to build HEMS environment instead of existing home appliances. Therefore, an HEMS, which performs with existing home appliances, is needed to prevent additional expenses due to the purchase of state-of-the-art devices. In this paper, an intelligent inference algorithm for EMS at home for non-power saving electronic equipment, called legacy devices, is proposed. The algorithm is based on the adaptive network fuzzy inference system (ANFIS) and has a subsystem that notifies retraining schedule to the ANFIS to increase the inference performance. This paper discusses the overview and the architecture of the system, especially in terms of the retraining schedule. In addition, the comparison results show that the proposed algorithm is more accurate than the classic ANFIS-based EMS system.

Original languageEnglish
Pages (from-to)779-785
Number of pages7
JournalTransactions of the Korean Institute of Electrical Engineers
Issue number5
Publication statusPublished - 2015 May 1


  • Adaptive network based fuzzy inference system (ANFIS)
  • Home energy management system (HEMS)
  • Legacy device
  • Training schedule notification

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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