Design and implementation of a high-fidelity AC metering network
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
ViridiScope: design and implementation of a fine grained power monitoring system for homes
Proceedings of the 11th international conference on Ubiquitous computing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ICT for green: how computers can help us to conserve energy
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
Communications of the ACM
Information Science & Technology in China: A Roadmap to 2050 (Chinese Academy of Sciences)
Information Science & Technology in China: A Roadmap to 2050 (Chinese Academy of Sciences)
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This paper formulates and studies the problem of accurately acquiring energy consumption information of physical objects influenced by human behavior into the cyber space. We formulate this input-sensing problem within the ternary computing framework, which allows real-world problem instances to be studied, with different constraints on human efforts, sensors needed, error bounds, and computational complexity. We focus on the input-sensing problem commonly found in the energy-efficient design and use of household electric devices (appliances). The main challenge is how to distinguish the electric currents of individual appliances even with only a single sensor. We recast this current disaggregating problem into a computer science problem called the approximate phase space learning problem. We develop a novel technique called the principal component manifold method to solve the learning problem. This method uses a linear manifold, spanned by the principal components, to approximate the phase space of an appliance. Experimental results show that our approach can trace the dynamic currents of the appliances with continuously variable load, and estimate the power consumption values with low errors.