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Electrical Load Classification with Open-Set Recognition

Author

Listed:
  • 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)

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.

Suggested Citation

  • Dániel István Németh & Kálmán Tornai, 2023. "Electrical Load Classification with Open-Set Recognition," Energies, MDPI, vol. 16(2), pages 1-14, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:800-:d:1031327
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    References listed on IDEAS

    as
    1. Sana Iqbal & Mohammad Sarfraz & Mohammad Ayyub & Mohd Tariq & Ripon K. Chakrabortty & Michael J. Ryan & Basem Alamri, 2021. "A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment," Sustainability, MDPI, vol. 13(13), pages 1-23, June.
    2. Noor, Sana & Yang, Wentao & Guo, Miao & van Dam, Koen H. & Wang, Xiaonan, 2018. "Energy Demand Side Management within micro-grid networks enhanced by blockchain," Applied Energy, Elsevier, vol. 228(C), pages 1385-1398.
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