Energy disaggregation based on semi-supervised matrix factorization using feedback information from consumers

Ayumu Miyasawa, Masako Matsumoto, Yu Fujimoto, Yasuhiro Hayashi

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

2 Citations (Scopus)

Abstract

Visualizations that depict a consumer's utilization of domestic appliances aid them in effectively thinking about energy conservation. Energy disaggregation aims to break down the total power consumed into the amount consumed by each individual appliance through the use of a single smart meter. This estimation technique provides detailed information about energy consumption and is cheaper than techniques that directly measure each appliance. In order to improve the accuracy of disaggregation, we utilize the consumer feedback information without placing any special burden on the consumer. We propose a semi-supervised shift-invariant weighted non-negative matrix factorization method with auxiliary feedback that records the on or off status of each individual appliance. The experimental results obtained by applying our proposed method to household datasets show that our proposed method and the associated auxiliary information generated contribute to the improvement of the disaggregation accuracy.

Original languageEnglish
Title of host publication2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538619537
DOIs
Publication statusPublished - 2017 Jul 1
Event2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Torino, Italy
Duration: 2017 Sep 262017 Sep 29

Publication series

Name2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings
Volume2018-January

Other

Other2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017
CountryItaly
CityTorino
Period17/9/2617/9/29

Keywords

  • energy disaggregation
  • energy management system
  • home appliances
  • machine learning
  • matrix factorization
  • smart house

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Energy Engineering and Power Technology
  • Computer Networks and Communications

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  • Cite this

    Miyasawa, A., Matsumoto, M., Fujimoto, Y., & Hayashi, Y. (2017). Energy disaggregation based on semi-supervised matrix factorization using feedback information from consumers. In 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings (pp. 1-6). (2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISGTEurope.2017.8260211