AAAI Publications, Workshops at the Thirtieth AAAI Conference on Artificial Intelligence

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Automatic Label Correction and Appliance Prioritization in Single Household Electricity Disaggregation
Mark Valovage, Maria Gini

Last modified: 2016-03-29

Abstract


Electricity disaggregation focuses on classification ofindividual appliances by monitoring aggregate electricalsignals. In this paper we present a novel algorithmto automatically correct labels, discard contaminatedtraining samples, and boost signal to noise ratio throughhigh frequency noise reduction. We also propose amethod for prioritized classification which classifies applianceswith the most intense signals first. When testedon four houses in Kaggles Belkin dataset, these methodsautomatically relabel over 77% of all training samplesand decrease error rate by an average of 45% in bothreal power and high frequency noise classification.

Keywords


CSAI: Modeling And Prediction Of Dynamic And Spatiotemporal Phenomena And Systems; ML: Classification; ML: Clustering; ML: Data Mining and Knowledge Discovery; ML: Time-Series/Data Streams; ML: Supervised Learning (Other)

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