Energy disaggregation based on semi-binary NMF

Masako Matsumoto, Yu Fujimoto, Yasuhiro Hayashi

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

5 Citations (Scopus)

Abstract

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
Pages401-414
Number of pages14
Volume9729
ISBN (Print)9783319419190
DOIs
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)
Volume9729
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016
CountryUnited States
CityNew York
Period16/7/1616/7/21

Fingerprint

Disaggregation
Energy Consumption
Energy utilization
Binary
Energy
Visualization
Renewable energy resources
Energy management systems
Non-negative Matrix Factorization
Energy Management
Renewable Energy
Electric circuit breakers
Factorization
Power Consumption
Electric power utilization
Monitoring
Sensor
Resources
Evaluate
Sensors

Keywords

  • Energy disaggregation
  • Semi-Binary NMF

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Matsumoto, M., Fujimoto, Y., & Hayashi, Y. (2016). Energy disaggregation based on semi-binary NMF. In Machine Learning and Data Mining in Pattern Recognition - 12th International Conference, MLDM 2016, Proceedings (Vol. 9729, pp. 401-414). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9729). Springer Verlag. https://doi.org/10.1007/978-3-319-41920-6_31

Energy disaggregation based on semi-binary NMF. / Matsumoto, Masako; Fujimoto, Yu; Hayashi, Yasuhiro.

Machine Learning and Data Mining in Pattern Recognition - 12th International Conference, MLDM 2016, Proceedings. Vol. 9729 Springer Verlag, 2016. p. 401-414 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9729).

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

Matsumoto, M, Fujimoto, Y & Hayashi, Y 2016, Energy disaggregation based on semi-binary NMF. in Machine Learning and Data Mining in Pattern Recognition - 12th International Conference, MLDM 2016, Proceedings. vol. 9729, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9729, Springer Verlag, pp. 401-414, 12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016, New York, United States, 16/7/16. https://doi.org/10.1007/978-3-319-41920-6_31
Matsumoto M, Fujimoto Y, Hayashi Y. Energy disaggregation based on semi-binary NMF. In Machine Learning and Data Mining in Pattern Recognition - 12th International Conference, MLDM 2016, Proceedings. Vol. 9729. Springer Verlag. 2016. p. 401-414. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-41920-6_31
Matsumoto, Masako ; Fujimoto, Yu ; Hayashi, Yasuhiro. / Energy disaggregation based on semi-binary NMF. Machine Learning and Data Mining in Pattern Recognition - 12th International Conference, MLDM 2016, Proceedings. Vol. 9729 Springer Verlag, 2016. pp. 401-414 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{7ee329b7225f4460a058e375bfbfa702,
title = "Energy disaggregation based on semi-binary NMF",
abstract = "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.",
keywords = "Energy disaggregation, Semi-Binary NMF",
author = "Masako Matsumoto and Yu Fujimoto and Yasuhiro Hayashi",
year = "2016",
doi = "10.1007/978-3-319-41920-6_31",
language = "English",
isbn = "9783319419190",
volume = "9729",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "401--414",
booktitle = "Machine Learning and Data Mining in Pattern Recognition - 12th International Conference, MLDM 2016, Proceedings",
address = "Germany",

}

TY - GEN

T1 - Energy disaggregation based on semi-binary NMF

AU - Matsumoto, Masako

AU - Fujimoto, Yu

AU - Hayashi, Yasuhiro

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Energy disaggregation

KW - Semi-Binary NMF

UR - http://www.scopus.com/inward/record.url?scp=84979066958&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84979066958&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-41920-6_31

DO - 10.1007/978-3-319-41920-6_31

M3 - Conference contribution

SN - 9783319419190

VL - 9729

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 401

EP - 414

BT - Machine Learning and Data Mining in Pattern Recognition - 12th International Conference, MLDM 2016, Proceedings

PB - Springer Verlag

ER -