Abstract
This paper investigates how to classify household items such as televisions, kettles and refrigerators based only on their electricity usage profile every 15 minutes over a fixed interval of time. We address this time series classification problem through deriving a set of features that characterise the pattern of usage and the amount of power used when a device is on. We evaluate a wide range of classifiers on both the raw data and the derived feature set using both a daily and weekly usage profile and demonstrate that whilst some devices can be identified with a high degree of accuracy, others are very hard to disambiguate with this granularity of data.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bagnall, A.J., Janacek, G.J.: Clustering time series from arma models with clipped data. In: 10th International Conference on Knowledge Discovery in Data and Data Mining (ACM SIGKDD 2004), pp. 49–58 (2004)
Darby, S.: Making it obvious: designing feedback into energy consumption, pp. 685–696 (2001)
Janacek, G.J., Bagnall, A.J., Powell, M.: A likelihood ratio distance measure for the similarity between the fourier transform of time series. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 737–743. Springer, Heidelberg (2005)
Keogh, E., Kasetty, S.: On the need for time series data mining benchmarks: A survey and empirical demonstration. Data Mining and Knowledge Discovery 7(4), 349–371 (2003)
Keogh, E., Pazzani, M.: Scaling up dynamic time warping for datamining applications. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2000, pp. 285–289 (2000)
Mörchen, F.: Time series feature extraction for data mining using DWT and DFT, Tech. Report 3. Department of Mathematics and Computer Science Philipps-University Marburg (2003)
Mörchen, F., Mierswa, I., Ultsch, A.: Understandable models of music collections based on exhaustive feature generation with temporal statistics. In: 12th International Conference on Knowledge Discovery in Data and Data Mining (ACM SIGKDD 2006), pp. 882–891 (2006)
Department of Energy and Climate Change, Smart metering implementation programme, White paper, Department of Energy and Climate Change (July 2010)
Smyth, P.: Clustering sequences with hidden markov models. In: Advances in Neural Information Processing Systems, vol. 9 (1997)
Toshniwal, D.: Feature extraction from time series data. International Journal of Computational Methods in Sciences and Engineering 8(1), 99–110 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lines, J., Bagnall, A., Caiger-Smith, P., Anderson, S. (2011). Classification of Household Devices by Electricity Usage Profiles. In: Yin, H., Wang, W., Rayward-Smith, V. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol 6936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23878-9_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-23878-9_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23877-2
Online ISBN: 978-3-642-23878-9
eBook Packages: Computer ScienceComputer Science (R0)