Energy disaggregation based on smart metering data via semi-binary nonnegative matrix factorization

Ayumu Miyasawa, Yu Fujimoto, Yasuhiro Hayashi

研究成果: Article

1 引用 (Scopus)

抄録

The information measured by smart electricity meters in households enables consumers to understand the relationship between their behavior and the power consumption. This paper proposes an energy disaggregation method to estimate the individual-appliance power consumption utilizing the total power consumption data collected by smart meters without requiring additional sensors for supporting sustainable demand-side energy management. In addition, in order to enable appliance labeling and highly accurate estimation without submetered data of the target households, the utilization of auxiliary information such as consumer feedback is suggested. This scheme provides users the opportunity to explicitly point out incorrect current disaggregation results so as to improve forthcoming future decomposition accuracy. The accuracy of the proposed energy disaggregation framework is evaluated using real-world power consumption data sets.

元の言語English
ページ(範囲)547-558
ページ数12
ジャーナルEnergy and Buildings
183
DOI
出版物ステータスPublished - 2019 1 15

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Factorization
Electric power utilization
Smart meters
Energy management
Labeling
Electricity
Decomposition
Feedback
Sensors

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanical Engineering
  • Electrical and Electronic Engineering

これを引用

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abstract = "The information measured by smart electricity meters in households enables consumers to understand the relationship between their behavior and the power consumption. This paper proposes an energy disaggregation method to estimate the individual-appliance power consumption utilizing the total power consumption data collected by smart meters without requiring additional sensors for supporting sustainable demand-side energy management. In addition, in order to enable appliance labeling and highly accurate estimation without submetered data of the target households, the utilization of auxiliary information such as consumer feedback is suggested. This scheme provides users the opportunity to explicitly point out incorrect current disaggregation results so as to improve forthcoming future decomposition accuracy. The accuracy of the proposed energy disaggregation framework is evaluated using real-world power consumption data sets.",
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