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Supero: A sensor system for unsupervised residential power usage monitoring

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6 Author(s)
Dennis E. Phillips ; Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA ; Rui Tan ; Mohammad-Mahdi Moazzami ; Guoliang Xing
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As a key technology of home area networks in smart grids, fine-grained power usage monitoring may help conserve electricity. Several existing systems achieve this goal by exploiting appliances' power usage signatures identified in labor-intensive in situ training processes. Recent work shows that autonomous power usage monitoring can be achieved by supplementing a smart meter with distributed sensors that detect the working states of appliances. However, sensors must be carefully installed for each appliance, resulting in high installation cost. This paper presents Supero - the first ad hoc sensor system that can monitor appliance power usage without supervised training. By exploiting multisensor fusion and unsupervised machine learning algorithms, Supero can classify the appliance events of interest and autonomously associate measured power usage with the respective appliances. Our extensive evaluation in five real homes shows that Supero can estimate the energy consumption with errors less than 7.5%. Moreover, non-professional users can quickly deploy Supero with considerable flexibility.

Published in:

Pervasive Computing and Communications (PerCom), 2013 IEEE International Conference on

Date of Conference:

18-22 March 2013