Discovering routine behaviours in smart water meter data

Research output: Chapter in Book/Conference paperConference paper

1 Citation (Scopus)

Abstract

© 2015 IEEE. Smart water meters are being used on a large scale by water providers to record hourly water use of households. The time series data recorded by smart water meters provide real-time information about water use activities. This paper proposes an algorithm to automatically discover recurrent routine behaviours in smart water meter data. The recurrent routine behaviours characterize regular water use activities during consecutive hours, which occur multiple times in a period. Our algorithm differs from previous exact motif discovery algorithms because we discover frequently occurring short subsequences with variable length. Experiment on a real-world dataset collected from an inland town of Kalgoorlie-Boulder in Western Australia demonstrates that the proposed algorithm discovers useful recurrent routine behaviours of different lengths, which are relevant for domain experts.
Original languageEnglish
Title of host publication2015 IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2015
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-6
VolumeN/A
ISBN (Print)9781479980550
DOIs
Publication statusPublished - 2015
EventDiscovering routine behaviours in smart water meter data - Singapore
Duration: 1 Jan 2015 → …

Conference

ConferenceDiscovering routine behaviours in smart water meter data
Period1/01/15 → …

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water use
water
boulder
time series
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household

Cite this

Wang, J., Cardell-Oliver, R., & Liu, W. (2015). Discovering routine behaviours in smart water meter data. In 2015 IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2015 (Vol. N/A, pp. 1-6). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ISSNIP.2015.7106899
Wang, Jin ; Cardell-Oliver, Rachel ; Liu, Wei. / Discovering routine behaviours in smart water meter data. 2015 IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2015. Vol. N/A IEEE, Institute of Electrical and Electronics Engineers, 2015. pp. 1-6
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title = "Discovering routine behaviours in smart water meter data",
abstract = "{\circledC} 2015 IEEE. Smart water meters are being used on a large scale by water providers to record hourly water use of households. The time series data recorded by smart water meters provide real-time information about water use activities. This paper proposes an algorithm to automatically discover recurrent routine behaviours in smart water meter data. The recurrent routine behaviours characterize regular water use activities during consecutive hours, which occur multiple times in a period. Our algorithm differs from previous exact motif discovery algorithms because we discover frequently occurring short subsequences with variable length. Experiment on a real-world dataset collected from an inland town of Kalgoorlie-Boulder in Western Australia demonstrates that the proposed algorithm discovers useful recurrent routine behaviours of different lengths, which are relevant for domain experts.",
author = "Jin Wang and Rachel Cardell-Oliver and Wei Liu",
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language = "English",
isbn = "9781479980550",
volume = "N/A",
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Wang, J, Cardell-Oliver, R & Liu, W 2015, Discovering routine behaviours in smart water meter data. in 2015 IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2015. vol. N/A, IEEE, Institute of Electrical and Electronics Engineers, pp. 1-6, Discovering routine behaviours in smart water meter data, 1/01/15. https://doi.org/10.1109/ISSNIP.2015.7106899

Discovering routine behaviours in smart water meter data. / Wang, Jin; Cardell-Oliver, Rachel; Liu, Wei.

2015 IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2015. Vol. N/A IEEE, Institute of Electrical and Electronics Engineers, 2015. p. 1-6.

Research output: Chapter in Book/Conference paperConference paper

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N2 - © 2015 IEEE. Smart water meters are being used on a large scale by water providers to record hourly water use of households. The time series data recorded by smart water meters provide real-time information about water use activities. This paper proposes an algorithm to automatically discover recurrent routine behaviours in smart water meter data. The recurrent routine behaviours characterize regular water use activities during consecutive hours, which occur multiple times in a period. Our algorithm differs from previous exact motif discovery algorithms because we discover frequently occurring short subsequences with variable length. Experiment on a real-world dataset collected from an inland town of Kalgoorlie-Boulder in Western Australia demonstrates that the proposed algorithm discovers useful recurrent routine behaviours of different lengths, which are relevant for domain experts.

AB - © 2015 IEEE. Smart water meters are being used on a large scale by water providers to record hourly water use of households. The time series data recorded by smart water meters provide real-time information about water use activities. This paper proposes an algorithm to automatically discover recurrent routine behaviours in smart water meter data. The recurrent routine behaviours characterize regular water use activities during consecutive hours, which occur multiple times in a period. Our algorithm differs from previous exact motif discovery algorithms because we discover frequently occurring short subsequences with variable length. Experiment on a real-world dataset collected from an inland town of Kalgoorlie-Boulder in Western Australia demonstrates that the proposed algorithm discovers useful recurrent routine behaviours of different lengths, which are relevant for domain experts.

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Wang J, Cardell-Oliver R, Liu W. Discovering routine behaviours in smart water meter data. In 2015 IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2015. Vol. N/A. IEEE, Institute of Electrical and Electronics Engineers. 2015. p. 1-6 https://doi.org/10.1109/ISSNIP.2015.7106899