Electrical Load Classification with Open-Set Recognition

Dániel István Németh () and Kálmán Tornai
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Dániel István Németh: Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary
Kálmán Tornai: Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary

Energies, 2023, vol. 16, issue 2, 1-14

Abstract: Full utilization of renewable energy resources is a difficult task due to the constantly changing demand-side load of the electrical grid. Demand-side management would solve this crucial problem. To enable demand-side management, knowledge about the composition of the grid load is required, as well as the ability to schedule individual loads. There are proposed Smart Plugs presented in the literature capable of such tasks. The problem, however, is that these methods lack the ability to detect if a previously unseen electrical load is connected. Misclassification of such loads presents a problem for load estimation and scheduling. Open-set recognition methods solve this problem by providing a way to detect samples not belonging to any class used during the training of the classifier. This paper evaluates the novel application of open-set recognition methods to the problem of load classification. Two approaches were examined, and both offer promising results. A Support Vector Machine based approach was first evaluated. The second, more robust method used a modified OpenMax-based algorithm to detect unseen loads.

Keywords: convolutional neural networks; electrical load classification; open-set recognition; smart grid; smart home; smart plug (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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