Appliance and State Recognition using Hidden Markov Models

Abstract—We asset about the analysis of electrical appliance consumption signatures for the identification task. We apply Hid-den Markov Models to appliance signatures for the identification of their category and of the most probable sequence of states. The electrical signatures are measured at low frequency (101 Hz) and are sourced from a specific database. We follow two predefined protocols for providing comparable results. Recovering informa-tion on the actual appliance state permits to potentially adopt energy saving measures, as switching off stand-by appliances or, generally speaking, changing their state. Moreover, in most of the cases appliance states are related to user activities: the user interaction usually involves a transition of the appliance state. Information about the state transition could be useful in Smart Home / Building Systems to reduce energy consumption and increase human comfort. We report the results of the classification tasks in terms of confusion matrices and accuracy rates. Finally, we present our application for a real-time data visualization and the recognition of the appliance category with its actual state