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Classification of Household Devices by Electricity Usage Profiles

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Intelligent Data Engineering and Automated Learning - IDEAL 2011 (IDEAL 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6936))

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.

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Lines, J., Bagnall, A., Caiger-Smith, P., Anderson, S. (2011). Classification of Household Devices by Electricity Usage Profiles. In: Yin, H., Wang, W., Rayward-Smith, V. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol 6936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23878-9_48

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  • DOI: https://doi.org/10.1007/978-3-642-23878-9_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23877-2

  • Online ISBN: 978-3-642-23878-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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