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

Jin Wang; Rachel Cardell-Oliver; Wei Liu

doi:10.1007/978-3-319-18032-8_41

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

Jin Wang, Rachel Cardell-Oliver, Wei Liu

Graduate Research School

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

2 Citations (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 language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining
Publisher	Springer International Publishing
Pages	522-533
Volume	9078
ISBN (Print)	9783319180328
DOIs	https://doi.org/10.1007/978-3-319-18032-8_41
Publication status	Published - 2015
Event	Efficient discovery of recurrent routine behaviours in smart meter time series by growing subsequences - Ho Chi Minh City, Vietnam Duration: 1 Jan 2015 → …

Conference

Conference	Efficient discovery of recurrent routine behaviours in smart meter time series by growing subsequences
Period	1/01/15 → …

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10.1007/978-3-319-18032-8_41

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title = "Efficient discovery of recurrent routine behaviours in smart meter time series by growing subsequences",

abstract = "{\textcopyright} 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.",

author = "Jin Wang and Rachel Cardell-Oliver and Wei Liu",

year = "2015",

doi = "10.1007/978-3-319-18032-8_41",

language = "English",

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volume = "9078",

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booktitle = "Advances in Knowledge Discovery and Data Mining",

publisher = "Springer International Publishing",

note = "Efficient discovery of recurrent routine behaviours in smart meter time series by growing subsequences ; Conference date: 01-01-2015",

}

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

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AU - Cardell-Oliver, Rachel

AU - Liu, Wei

PY - 2015

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N2 - © 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.

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.

U2 - 10.1007/978-3-319-18032-8_41

DO - 10.1007/978-3-319-18032-8_41

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VL - 9078

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EP - 533

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PB - Springer International Publishing

T2 - Efficient discovery of recurrent routine behaviours in smart meter time series by growing subsequences

Y2 - 1 January 2015

ER -

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

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