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Novelty Detection in Human Behavior through Analysis of Energy Utilization

Novelty Detection in Human Behavior through Analysis of Energy Utilization

Chao Chen, Diane J. Cook
Copyright: © 2013 |Pages: 21
ISBN13: 9781466636828|ISBN10: 1466636823|EISBN13: 9781466636835
DOI: 10.4018/978-1-4666-3682-8.ch004
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MLA

Chen, Chao, and Diane J. Cook. "Novelty Detection in Human Behavior through Analysis of Energy Utilization." Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security, edited by Hans W. Guesgen and Stephen Marsland, IGI Global, 2013, pp. 65-85. https://doi.org/10.4018/978-1-4666-3682-8.ch004

APA

Chen, C. & Cook, D. J. (2013). Novelty Detection in Human Behavior through Analysis of Energy Utilization. In H. Guesgen & S. Marsland (Eds.), Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security (pp. 65-85). IGI Global. https://doi.org/10.4018/978-1-4666-3682-8.ch004

Chicago

Chen, Chao, and Diane J. Cook. "Novelty Detection in Human Behavior through Analysis of Energy Utilization." In Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security, edited by Hans W. Guesgen and Stephen Marsland, 65-85. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3682-8.ch004

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Abstract

The value of smart environments in understanding and monitoring human behavior has become increasingly obvious in the past few years. Using data collected from sensors in these environments, scientists have been able to recognize activities that residents perform and use the information to provide context-aware services and information. However, less attention has been paid to monitoring and analyzing energy usage in smart homes, despite the fact that electricity consumption in homes has grown dramatically. In this chapter, the authors demonstrate how energy consumption relates to human activity through verifying that energy consumption can be predicted based on the activity that is being performed. The authors then automatically identify novelties in human behavior by recognizing outliers in energy consumption generated by the residents in a smart environment. To validate these approaches, they use real energy data collected in their CASAS smart apartment testbed and analyze the results for two different data sets collected in this smart home.

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