Virtual power sensing based on a multiple-hypothesis sequential test

Virtual-Sensing, which is achieved through the disaggregation of composite power metering signals, is a solution towards achieving fine-grained smart power monitoring. In this work we discuss the challenging issues in Virtual-Sensing, introduce and ultimately combine the Hidden Markov Model and the Edge-based methods. The resulting solution, based on a Multiple-hypothesis Sequential Probability Ratio Test, combines the advantages of the two methods and delivers significant improvement in disaggregation performance. A robust version of the test is also proposed to filter the impulse noise common in real-time monitoring of the plug-in loads power consumption.