Development of prediction-based operation planning method for domestic air-conditioner with adaptive learning of installation environment

Ryoichi Kuroha, Yu Fujimoto, Wataru Hirohashi, Yoshiharu Amano, Shinichi Tanabe, Yasuhiro Hayashi

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

1 Citation (Scopus)

Abstract

The world is more aware of the need for saving energy because of increasing world energy consumption and environmental problems. To promote saving energy in the domestic field, the use of home energy management systems (HEMSs) is rapidly spreading. The HEMS which has automatically controlling function can control domestic electrical appliances including air-conditioners (ACs). In this research, we focus on AC operation plans to improve thermal comfort and reduce electricity costs for residents. However, AC control planning is generally a difficult task because the operation results greatly depend on the environmental characteristics in which the HEMS is installed. To solve this problem, we proposed an AC planning method that accounts for environmental characteristics and uncertainty in prediction by using historical data.

Original languageEnglish
Title of host publication2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538628904
DOIs
Publication statusPublished - 2017 Oct 26
Event2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017 - Washington, United States
Duration: 2017 Apr 232017 Apr 26

Other

Other2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017
CountryUnited States
CityWashington
Period17/4/2317/4/26

Keywords

  • AC Operation Planning
  • Air conditioner (AC)
  • Home Energy Management System (HEMS)
  • Particle Swarm Optimization (PSO)
  • Predicted Mean Vote (PMV)
  • Smart House
  • Support Vector Regression (SVR)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Control and Optimization

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  • Cite this

    Kuroha, R., Fujimoto, Y., Hirohashi, W., Amano, Y., Tanabe, S., & Hayashi, Y. (2017). Development of prediction-based operation planning method for domestic air-conditioner with adaptive learning of installation environment. In 2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017 [8085983] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISGT.2017.8085983