Time-Lapse Image Method for Classifying Appliances in Nonintrusive Load Monitoring

Joonho Seon, Youngghyu Sun, Soohyun Kim and Jinyoung Kim
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Joonho Seon: Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, Korea
Youngghyu Sun: Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, Korea
Soohyun Kim: Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, Korea

Energies, 2021, vol. 14, issue 22, 1-19

Abstract: In this paper, a time-lapse image method is proposed to improve the classification accuracy for multistate appliances with complex patterns based on nonintrusive load monitoring (NILM). A log-likelihood ratio detector with a maxima algorithm was applied to construct a real-time event detection of home appliances. Moreover, a novel image-combining method was employed to extract information from the data based on the Gramian angular field (GAF) and recurrence plot (RP) transformations. From the simulation results, it was confirmed that the classification accuracy can be increased by up to 30% with the proposed method compared with the conventional approaches in classifying multistate appliances.

Keywords: nonintrusive load monitoring; Gramian angular field; recurrence plot; overlapping sliding window; multistate appliances (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: 2021
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