The Input-Sensing Problem in Ternary Computing and Its Application in Household Energy-Saving

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.