Label correction and event detection for electricity disaggregation

Mark Valovage, Maria Gini

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

5 Scopus citations

Abstract

Electricity disaggregation focuses on identifying individual appliances from one or more aggregate signals. By reporting detailed appliance usage to consumers, disaggregation has the potential to significantly reduce electrical waste in residential and commercial sectors. However, application of existing methods is limited by two critical shortcomings. First, supervised learning methods implicitly assume error- free labels in training data, an unrealistic expectation for imperfectly-labeled consumer data. Second, supervised and unsupervised learning methods require parameters to be tuned to individual appliances and/or datasets, limiting widespread application. To address these limitations, this paper introduces the implementation of Bayesian changepoint detection (BCD) with necessary adaptations to electricity disaggregation. We introduce an algorithm to effectively apply BCD to automatically correct labels. We then apply BCD to event detection to identify transitions between appliances' on and off states. Performance is evaluated using 3 publicly available datasets containing over 250 appliances across 11 houses. Results show both BCD applications are competi-Tive and in some cases outperform existing state-of-The-Art methods without the need for parameter tuning, advancing disaggregation towards widespread, real-world deployment.

Original languageEnglish (US)
Title of host publication16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017
EditorsSanmay Das, Edmund Durfee, Kate Larson, Michael Winikoff
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages990-998
Number of pages9
ISBN (Electronic)9781510855076
StatePublished - Jan 1 2017
Event16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017 - Sao Paulo, Brazil
Duration: May 8 2017May 12 2017

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume2
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Other

Other16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017
CountryBrazil
CitySao Paulo
Period5/8/175/12/17

Keywords

  • Change detection
  • Electricity disaggregation
  • Event detection
  • Label correction
  • Supervised unsupervised learning

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  • Cite this

    Valovage, M., & Gini, M. (2017). Label correction and event detection for electricity disaggregation. In S. Das, E. Durfee, K. Larson, & M. Winikoff (Eds.), 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017 (pp. 990-998). (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS; Vol. 2). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).