Journal
PERVASIVE AND MOBILE COMPUTING
Volume 9, Issue 1, Pages 161-175Publisher
ELSEVIER
DOI: 10.1016/j.pmcj.2012.10.004
Keywords
Smart environments; Machine learning; Energy; Anomaly detection
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Society is becoming increasingly aware of the impact that our lifestyle choices make on energy usage and the environment. As a result, research attention is being directed toward green technology, environmentally-friendly building designs, and smart grids. This paper looks at the user side of sustainability. In particular, it looks at energy consumption in everyday home environments to examine the relationship between behavioral patterns and energy consumption. It first demonstrates how data mining techniques may be used to find patterns and anomalies in smart home-based energy data. Next, it describes a method to correlate home-based activities with electricity usage. Finally, it describes how this information could inform users about their personal energy consumption and to support activities in a more energy-efficient manner. These approaches are validated by using real energy data collected in a set of smart home testbeds. (C) 2012 Elsevier B.V. All rights reserved.
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