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A Habit Detection Algorithm (HDA) for Discovering Recurrent Patterns in Smart Meter Time Series

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Book cover Big Data Analytics in the Social and Ubiquitous Context (SENSEML 2015, MUSE 2014, MSM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9546))

Abstract

Conserving water is a critical problem and characterising how households in communities use water is a first step for reducing consumption. This paper introduces a method for discovering habits in smart water meter time series. Habits are household activities that recur in a predictable way, such as watering the garden at 6 am twice a week. Discovering habit patterns automatically is a challenging data mining task. Habit patterns are not only periodic, nor only seasonal, and they may not be frequent. Their recurrences are partial periodic patterns with a very large number of candidates. Further, the recurrences in real data are imperfect, making accurate matching of observations with proposed patterns difficult. The main contribution of this paper is an efficient, robust and accurate Habit Detection Algorithm (HDA) for discovering regular activities in smart meter time series with evaluation the performance of the algorithm and its ability to discover valuable insights from real-world data sets.

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Notes

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    http://reports.weforum.org/global-risks-2015/.

  2. 2.

    http://www.r-project.org/.

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Acknowledgments

This research is funded by the Cooperative Research Centre for Water Sensitive Cities under project C5.1. The author would like to thank H. Gigney, S. Atkinson, G. Peach and R. Pickering at the Water Corporation of Western Australia for the smart meter datasets and for their advice on interpreting them. Thanks also to Eneldo Loza Mencia, Jin Wang and the anonymous reviewers for comments that greatly improved the manuscript. This research has been approved by the Human Research Ethics Office (HREO) of the University of Western Australia (RA/4/1/6253).

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Authors and Affiliations

  1. CRC for Water Sensitive Cities, PO Box 8000, Clayton, VIC, 3800, Australia

    Rachel Cardell-Oliver

  2. The University of Western Australia, 35 Stirling Highway, Crawley, 6009, Australia

    Rachel Cardell-Oliver

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  1. Rachel Cardell-Oliver
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Correspondence to Rachel Cardell-Oliver .

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Editors and Affiliations

  1. University of Kassel, Kassel, Hessen, Germany

    Martin Atzmueller

  2. BMW Technology Group, Chicago, Illinois, USA

    Alvin Chin

  3. Knowledge Engineering, Technische Universität Darmstadt, Darmstadt, Hessen, Germany

    Frederik Janssen

  4. TU Darmstadt, Darmstadt, Germany

    Immanuel Schweizer

  5. Graz University of Technology, Graz, Austria

    Christoph Trattner

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Cardell-Oliver, R. (2016). A Habit Detection Algorithm (HDA) for Discovering Recurrent Patterns in Smart Meter Time Series. In: Atzmueller, M., Chin, A., Janssen, F., Schweizer, I., Trattner, C. (eds) Big Data Analytics in the Social and Ubiquitous Context. SENSEML MUSE MSM 2015 2014 2014. Lecture Notes in Computer Science(), vol 9546. Springer, Cham. https://doi.org/10.1007/978-3-319-29009-6_6

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  • DOI: https://doi.org/10.1007/978-3-319-29009-6_6

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-29009-6

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