Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cardell-Oliver, R.: Water use signature patterns for analyzing household consumption using medium resolution meter data. Water Resources Research 49(12), 8589–8599 (2013)
Lin, J., Keogh, E., Lonardi, S., Patel, P.: Finding motifs in time series. In: Proceedings of the 2nd Workshop on Temporal Data Mining, at the 8th ACM SIGKDD, Alberta, Canada, pp. 53–68 (2002)
Patel, P., Keogh, E., Lin, J., Lonardi, S.: Mining motifs in massive time series databases. In: 2002 IEEE ICDM, pp. 370–377 (2002)
Mueen, A., Keogh E.J., Zhu, Q., Cash, S., Westover, B.: Exact discovery of time series motifs. In: SIAM International Conference on Data Mining. American Statistical Association (ASA) (2009)
Mueen, A., Keogh, E.: Online discovery and maintenance of time series motifs. In: 16th ACM SIGKDD, New York, USA, pp. 1089–1098 (2010)
Mueen, A.: Enumeration of time series motifs of all lengths. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 547–556, December 2013
Powers, D.M.W.: Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. International Journal of Machine Learning Technology 2(1), 37–63 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, J., Cardell-Oliver, R., Liu, W. (2015). Efficient Discovery of Recurrent Routine Behaviours in Smart Meter Time Series by Growing Subsequences. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9078. Springer, Cham. https://doi.org/10.1007/978-3-319-18032-8_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-18032-8_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18031-1
Online ISBN: 978-3-319-18032-8
eBook Packages: Computer ScienceComputer Science (R0)