Efficient discovery of recurrent routine behaviours in smart meter time series by growing subsequences

Jin Wang, Rachel Cardell-Oliver, Wei Liu

Research output: Chapter in Book/Conference paper › Conference paper

1 Citation (Scopus)

Abstract

© Springer International Publishing Switzerland 2015. Data mining techniques have been developed to automatically learn consumption behaviours of households from smart meter data. In this paper, recurrent routine behaviours are introduced to characterize regular consumption activities in smart meter time series. A novel algorithm is proposed to efficiently discover recurrent routine behaviours in smart meter time series by growing subsequences. We evaluate the proposed algorithm on synthetic data and demonstrate the recurrent routine behaviours extracted on a real-world dataset from the city of Kalgoorlie-Boulder in Western Australia.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
PublisherSpringer International Publishing
Pages522-533
Volume9078
ISBN (Print)9783319180328
DOIs
Publication statusPublished - 2015
EventEfficient discovery of recurrent routine behaviours in smart meter time series by growing subsequences - Ho Chi Minh City, Vietnam
Duration: 1 Jan 2015 → …

Conference

ConferenceEfficient discovery of recurrent routine behaviours in smart meter time series by growing subsequences
Period1/01/15 → …

Fingerprint

Smart meters
Time series
Data mining

Cite this

Wang, J., Cardell-Oliver, R., & Liu, W. (2015). Efficient discovery of recurrent routine behaviours in smart meter time series by growing subsequences. In Advances in Knowledge Discovery and Data Mining (Vol. 9078, pp. 522-533). Springer International Publishing. https://doi.org/10.1007/978-3-319-18032-8_41
Wang, Jin ; Cardell-Oliver, Rachel ; Liu, Wei. / Efficient discovery of recurrent routine behaviours in smart meter time series by growing subsequences. Advances in Knowledge Discovery and Data Mining. Vol. 9078 Springer International Publishing, 2015. pp. 522-533
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Wang, J, Cardell-Oliver, R & Liu, W 2015, Efficient discovery of recurrent routine behaviours in smart meter time series by growing subsequences. in Advances in Knowledge Discovery and Data Mining. vol. 9078, Springer International Publishing, pp. 522-533, Efficient discovery of recurrent routine behaviours in smart meter time series by growing subsequences, 1/01/15. https://doi.org/10.1007/978-3-319-18032-8_41

Efficient discovery of recurrent routine behaviours in smart meter time series by growing subsequences. / Wang, Jin; Cardell-Oliver, Rachel; Liu, Wei.

Advances in Knowledge Discovery and Data Mining. Vol. 9078 Springer International Publishing, 2015. p. 522-533.

Research output: Chapter in Book/Conference paper › Conference paper

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AB - © Springer International Publishing Switzerland 2015. Data mining techniques have been developed to automatically learn consumption behaviours of households from smart meter data. In this paper, recurrent routine behaviours are introduced to characterize regular consumption activities in smart meter time series. A novel algorithm is proposed to efficiently discover recurrent routine behaviours in smart meter time series by growing subsequences. We evaluate the proposed algorithm on synthetic data and demonstrate the recurrent routine behaviours extracted on a real-world dataset from the city of Kalgoorlie-Boulder in Western Australia.

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Wang J, Cardell-Oliver R, Liu W. Efficient discovery of recurrent routine behaviours in smart meter time series by growing subsequences. In Advances in Knowledge Discovery and Data Mining. Vol. 9078. Springer International Publishing. 2015. p. 522-533 https://doi.org/10.1007/978-3-319-18032-8_41

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