Home energy management based on Bayesian network considering resident convenience

Tomoaki Shoji, Wataru Hirohashi, Yu Fujimoto, Yasuhiro Hayashi

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

7 Citations (Scopus)

Abstract

Total electricity consumption in Japan increased rapidly and the power consumption per household is also continuing to increase. The framework of demand response (DR) to promote the reduction of electricity consumption in the household sector by regulating the price of the electricity will be introduced in the future. In this situation, residents must operate their appliances so as not to affect much to their lifestyles while taking into account the power cost. A home energy management system (HEMS) will have an essential role to control appliances such as air conditioners (ACs), battery energy storage systems (BESSs), electric vehicles (EVs), and heat pump water heaters (HPWHs) and automatically match their operations to the behavior of a resident when the electricity price changes. In this study, a Bayesian network, a fundamental tool of machine learning, is adapted to an HEMS to learn the behavior of the resident and appropriate operations of controllable appliances.

Original languageEnglish
Title of host publication2014 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2014 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479935611
DOIs
Publication statusPublished - 2014 Nov 17
Event2014 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2014 - Durham, United Kingdom
Duration: 2014 Jul 72014 Jul 10

Publication series

Name2014 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2014 - Conference Proceedings

Conference

Conference2014 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2014
CountryUnited Kingdom
CityDurham
Period14/7/714/7/10

Keywords

  • Bayesian network (BN)
  • demand response (DR)
  • home energy management system (HEMS)
  • machine learning
  • smart grid

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Control and Systems Engineering
  • Statistics and Probability

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