Classification of household devices by electricity usage profiles

Authors:
Jason Lines;Anthony Bagnall;Patrick Caiger-Smith;Simon Anderson
Affiliations:
School of Computing Sciences, University of East Anglia, Norwich, UK;School of Computing Sciences, University of East Anglia, Norwich, UK;Green Energy Options, Hardwick, Cambridge, UK;Green Energy Options, Hardwick, Cambridge, UK
Venue:
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Year:
2011

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Abstract

This paper investigates how to classify household items such as televisions, kettles and refrigerators based only on their electricity usage profile every 15 minutes over a fixed interval of time. We address this time series classification problem through deriving a set of features that characterise the pattern of usage and the amount of power used when a device is on. We evaluate a wide range of classifiers on both the raw data and the derived feature set using both a daily and weekly usage profile and demonstrate that whilst some devices can be identified with a high degree of accuracy, others are very hard to disambiguate with this granularity of data.