Energy disaggregation based on semi-binary NMF

Masako Matsumoto*, Yu Fujimoto, Yasuhiro Hayashi

*Corresponding author for this work

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

7 Citations (Scopus)


The large-scale introduction of renewable energy resources will cause instability in the power supply. Residential energy management systems will be even more important in the near future. An important function of such systems is visualization of appliance-wise energy consumption; residents will be able to consciously avoid unnecessary consumption behavior. However, visualization requires sensors to measure appliance-wise energy consumption and is generally a costly task. In this paper, an unsupervised method for nonintrusive appliance load monitoring based on a semi-binary non-negative matrix factorization model is proposed. This framework utilizes the total power consumption patterns measured at the circuit breaker panel in a house, and derives disaggregated appliance-wise energy consumption. In the proposed approach, the energy consumption of individual appliances is estimated by considering the appliance-specific variances based on an aggregated energy consumption data set. The authors implement the proposed method and evaluate disaggregation accuracy using real world data sets.

Original languageEnglish
Title of host publicationMachine Learning and Data Mining in Pattern Recognition - 12th International Conference, MLDM 2016, Proceedings
PublisherSpringer Verlag
Number of pages14
ISBN (Print)9783319419190
Publication statusPublished - 2016
Event12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016 - New York, United States
Duration: 2016 Jul 162016 Jul 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)03029743
ISSN (Electronic)16113349


Other12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016
Country/TerritoryUnited States
CityNew York


  • Energy disaggregation
  • Semi-Binary NMF

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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