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Improving Automatic Classifiers Through Interaction Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Unsupervised Detection of Unusual Behaviors from Smart Home Energy Data

Authors : Welma Pereira, Alois Ferscha, Klemens Weigl

Published in: Artificial Intelligence and Soft Computing

Publisher: Springer International Publishing

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Abstract

In this paper the potentials of identifying unusual user behaviors and changes of behavior from smart home energy meters are investigated. We compare the performance of the classical change detection Page-Hinkley test (PHT) with a new application of a self-adaptive stream clustering algorithm to detect novelties related to the time of use of appliances at home. With the use of annotated data, the true positive rate of the clustering-based method outperformed the PHT by at least 20 %. Moreover the method was able to identify behavior changes related to time shifts and replacement of appliances. The motivation for this study is based on the need for identifying and guiding behavior changes that can reduce energy consumption, and use this knowledge in the development of systems that can raise just-in-time warnings to save energy (e.g. avoid stand-by modes), and guide sustainable behavior changes.

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Literature
1.
Hekler, E.B., Klasnja, P., Froehlich, J.E., Buman, M.P.: Mind the theoretical gap: interpreting, using, and developing behavioral theory in HCI research. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3307–3316. ACM, New York (2013)
2.
Danner, U.N., Aarts, H., Vries, N.K.: Habit vs. intention in the prediction of future behaviour: the role of frequency, context stability and mental accessibility of past behaviour. Br. J. Soc. Psychol. 47, 245–265 (2008)CrossRef
3.
4.
Klasnja, P., Consolvo, S., Pratt, W.: How to evaluate technologies for health behavior change in HCI research. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3063–3072. ACM, New York (2011)
5.
Hoelzl, G., Halbmayer, P., Rogner, H., Xue, C., Ferscha, A.: On the utilization of smart gadgets for energy aware sensitive behavior. In: The 8th International Conference on Digital Society, pp. 192–198. ACM (2014)
6.
Halbmayer, P., Hoelzl, G., Ferscha, A.: A dynamic service module oriented framework for real-world situation representation. In: The 6th International Conference on Adaptive and Self-Adaptive Systems and Applications, pp. 79–84 (2014)
7.
Kranen, P., Assenty, I., Baldauf, C., Seidl, T.: Self-adaptive anytime stream clustering. In: Ninth IEEE International Conference on Data Mining, pp. 249–258 (2009)
8.
9.
10.
Pimentel, M.A.F., Clifton, D.A., Clifton, L., Tarassenko, L.: A review of novelty detection. Sig. Process 99, 215–249 (2014)CrossRef
11.
Tanuja, G., Rahayu, D.A.P., Seetharam, D.P., Kunnath, R., Kumar, A.P., Vijay, A., Husain, S.A., Kalyanaraman, S.: SocketWatch: an autonomous appliance monitoring system. In: IEEE International Conference on Pervasive Computing and Communications, pp. 38–43. IEEE (2014)
12.
Vallim, R.M.M., Andrade Filho, J.A., De Mello, R.F., De Carvalho, A.C.P.L.F.: Online behavior change detection in computer games. Expert Syst. Appl. 40, 6258–6265 (2013)CrossRef
13.
Aggarwal, C.: A Survey of Change Diagnosis Algorithms in Evolving Data Streams, vol. 31, pp. 85–102. Springer, Heidelberg (2007)
14.
Zaidi, A.A., Kupzog, F., Zia, T., Palensky, P.: Load recognition for automated demand response in microgrids. In: 36th Annual Conference on IEEE Industrial Electronics Society, pp. 2442–2447 (2010)
15.
Wang, M., Wang, X.S.: Efficient evaluation of composite correlations for streaming time series. In: Dong, G., Tang, C., Wang, W. (eds.) WAIM 2003. LNCS, vol. 2762, pp. 369–380. Springer, Heidelberg (2003)CrossRef
16.
Chen, Y.C., Peng, W.C., Huang, J.L., Lee, W.C.: Significant correlation pattern mining in smart homes. ACM Trans. Intell. Syst. Technol. 6, 35:1–35:23 (2015)
17.
Rollins, S., Banerjee, N.: Using rule mining to understand appliance energy consumption patterns. In: IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 29–37 (2014)
18.
Chamber, Y., Garg, A., Mithal, V., Brugere, I., Lau, M., Boriah, S., Potter, C.: A novel time series based approach to detect gradual vegetation changes in forests. In: Proceedings of the 2011 NASA Conference on Intelligent Data Understanding (CIDU) (2011)
19.
Le, V.-D., Scholten, H., Havinga, P.J.M.: Online change detection for energy-efficient mobile crowdsensing. In: Awan, I., Younas, M., Franch, X., Quer, C. (eds.) MobiWIS 2014. LNCS, vol. 8640, pp. 1–16. Springer, Heidelberg (2014)
20.
Aztiria, A., Farhadi, G., Aghajan, H.: User behavior shift detection in intelligent environments. In: Bravo, J., Hervás, R., Rodríguez, M. (eds.) IWAAL 2012. LNCS, vol. 7657, pp. 90–97. Springer, Heidelberg (2012)CrossRef
21.
Bourgeois, J., van der Linden, J., Kortuem, G., Price, B.A., Rimmer, C.: Conversations with my washing machine: an in-the-wild study of demand shifting with self-generated energy. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 459–470 (2014)
22.
Kawamoto, K., Tanaka, T., Kuriyama, H.: Your activity tracker knows when you quit smoking. In: Proceedings of the 2014 ACM International Symposium on Wearable Computers, pp. 107–110 (2014)
23.
Doryab, A., Min, J.K., Wiese, J., Zimmerman, J., Hong, J.: Detection of behavior change in people with depression. In: AAAI Workshops (2014)
24.
Darby, S.: The effectiveness of feedback on energy consumption. A review for DEFRA of the literature on metering, billing and direct displays. Technical report, Environmental Change Inst., Univ. Oxford, Oxford, U.K. (2006)
25.
Tsang, F., Burge, P., Diepeveen, S., Guerin, B., Drabble, S., Bloom, E.: What works in changing energy-using behaviours in the home? A rapid evidence assessment: final report. UK Department of Energy and Climate Change. London, United Kingdom (2012)
26.
Zhou, K., Yang, S.: Understanding household energy consumption behavior: the contribution of energy big data analytics. Renew. Sustain. Ener. Rev. 56, 810–819 (2016)CrossRef
27.
Kjeldskov, J., Skov, M.B., Paay, J., Pathmanathan, R.: Using mobile phones to support sustainability: a field study of residential electricity consumption. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2347–2356. ACM (2012)
28.
Chen, Y.C., Ko, Y.L., Peng, W.C.: An intelligent system for mining usage patterns from appliance data in smart home environment. In: Technologies and Applications of Artificial Intelligence (TAAI), pp. 319–322 (2012)
29.
Kjeldskov, J., Skov, M.B., Paay, J., Lund, D., Madsen, T., Nielsen, M.: Eco-forecasting for domestic electricity use. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 1985–1988. ACM (2015)
30.
Hollis, V., Konrad, A., Whittaker, S.: Change of heart: emotion tracking to promote behavior change. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 2643–2652. ACM (2015)
31.
Drachen, A., Thurau, C., Sifa, R., Bauckhage, C.: A comparison of methods for player clustering via behavioral telemetry. CoRR. abs/1407.3950 (2014)
Metadata
Title
Unsupervised Detection of Unusual Behaviors from Smart Home Energy Data
Authors
Welma Pereira
Alois Ferscha
Klemens Weigl
Copyright Year
2016
Book
Artificial Intelligence and Soft Computing

Print ISBN: 978-3-319-39383-4

Electronic ISBN: 978-3-319-39384-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-39384-1_46

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