Appliance recognition with combined single- and multi-label approaches.

The problem of appliance recognition is one of the most relevant issues in the field of Non-Intrusive-Load-Monitoring; its importance has led, in recent years, to the development of innovative techniques to try to solve it. The use of methods such as V-I trajectory, Fryze Theory Decomposition and Weighted Recurrence Graph have proved effective in recognising both single (Single Label) and multiple active appliances (Multi Label). This paper presents a new way of approaching the problem by unifying Single Label and Multi Label learning paradigms. The proposed approach exploits feature extraction techniques which allow the detection of both activated/deactivated appliances and all active appliances given aggregate current signal. We evaluate the proposed approach on a PLAID dataset. The obtained results indicate combining single-label and mult-label learning strategies for appliance recognition provides improved classification results with an F-score of 0.91.