Automatic label correction and appliance prioritization in single household electricity disaggregation

Mark Valovage, Maria Gini

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

2 Scopus citations


Electricity disaggregation focuses on classification of individual appliances by monitoring aggregate electrical signals. In this paper we present a novel algorithm to automatically correct labels, discard contaminated training samples, and boost signal to noise ratio through high frequency noise reduction. We also propose a method for prioritized classification which classifies appliances with the most intense signals first. When tested on four houses in Kaggles Belkin dataset, these methods automatically relabel over 77% of all training samples and decrease error rate by an average of 45% in both real power and high frequency noise classification.

Original languageEnglish (US)
Title of host publicationWS-16-01
Subtitle of host publicationArtificial Intelligence Applied to Assistive Technologies and Smart Environments; WS-16-02: AI, Ethics, and Society; WS-16-03: Artificial Intelligence for Cyber Security; WS-16-04: Artificial Intelligence for Smart Grids and Smart Buildings; WS-16-05: Beyond NP; WS-16-06: Computer Poker and Imperfect Information Games; WS-16-07: Declarative Learning Based Programming; WS-16-08: Expanding the Boundaries of Health Informatics Using AI; WS-16-09: Incentives and Trust in Electronic Communities; WS-16-10: Knowledge Extraction from Text; WS-16-11: Multiagent Interaction without Prior Coordination; WS-16-12: Planning for Hybrid Systems; WS-16-13: Scholarly Big Data: AI Perspectives, Challenges, and Ideas; WS-16-14: Symbiotic Cognitive Systems; WS-16-15: World Wide Web and Population Health Intelligence
PublisherAI Access Foundation
Number of pages8
ISBN (Electronic)9781577357599
StatePublished - Jan 1 2016
Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States
Duration: Feb 12 2016Feb 17 2016

Publication series

NameAAAI Workshop - Technical Report
VolumeWS-16-01 - WS-16-15


Other30th AAAI Conference on Artificial Intelligence, AAAI 2016
CountryUnited States

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