Controlling Home and Office Appliances with Smart Phones
IEEE Pervasive Computing
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IEEE Transactions on Consumer Electronics
An Approach of Household Power Appliance Monitoring Based on Machine Learning
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Towards automatic classification of private households using electricity consumption data
BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
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Most current home automation systems are confined to a timer-based control of light and heating in order to improve the user's comfort. Additionally, these systems can be used to achieve energy savings, e.g., by turning the appliances off during the user's absence. The configuration of such systems, however, represents a major hindrance to their widespread deployment, as each connected appliance must be individually configured and assigned an operation schedule. The detection of active appliances as well as their current operating mode represents an enabling technology on the way to truly smart buildings. Once appliance identities are known, the devices can be deactivated to save energy or automatically controlled to increase the user's comfort. In this paper, we propose an approach to have buildings informed about the presence and activity of electric appliances. It relies on distributed high-frequency measurements of electrical voltage and current and feature extraction process that distills the collected data into distinct features. We utilize a supervised machine learning algorithm to classify readings into the underlying device type as well as its operation mode, which achieves an accuracy of up to 99.8%.