Manolo Manca, Anthony Faustine, Lucas Pereira
6th International Workshop on Non-Intrusive Load Monitoring (NILM ’22), November 9–10, 2022, Boston, MA, USA; doi: 10.1145/3563357.3566153
Publication year: 2022


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 recognizing 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 that 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 multi-label learning strategies for appliance recognition provides improved classification results with an F-score of 0.91.