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.File | Dimensione | Formato | |
---|---|---|---|
3563357.3566153.pdf
accesso aperto
Tipologia:
versione editoriale (VoR)
Dimensione
707.84 kB
Formato
Adobe PDF
|
707.84 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.