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.

Appliance recognition with combined single- and multi-label approaches

Manca, Marco Manolo;
2022-01-01

Abstract

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.
2022
9781450398909
appliance recognition; multi-label; NILM; single-label
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/433745
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