[논문]Temporal Patternization of Power Signatures for Appliance Classification in NILM

Kim, Hwan; Lim, Sungsu

doi:10.3390/en14102931

[해외논문] Temporal Patternization of Power Signatures for Appliance Classification in NILM 원문보기

Energies, v.14 no.10, 2021년, pp.2931 -

Kim, Hwan (Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Korea) , Lim, Sungsu (Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Korea)

Abstract ▼ AI-Helper

Non-Intrusive Load Monitoring (NILM) techniques are effective for managing energy and for addressing imbalances between the energy demand and supply. Various studies based on deep learning have reported the classification of appliances from aggregated power signals. In this paper, we propose a novel approach called a temporal bar graph, which patternizes the operational status of the appliances and time in order to extract the inherent features from the aggregated power signals for efficient load identification. To verify the effectiveness of the proposed method, a temporal bar graph was applied to the total power and tested on three state-of-the-art deep learning techniques that previously exhibited superior performance in image classification tasks-namely, Extreme Inception (Xception), Very Deep One Dimensional CNN (VDOCNN), and Concatenate-DenseNet121. The UK Domestic Appliance-Level Electricity (UK-DALE) and Tracebase datasets were used for our experiments. The results of the five-appliance case demonstrated that the accuracy and F1-score increased by 19.55% and 21.43%, respectively, on VDOCNN, and by 33.22% and 35.71%, respectively, on Xception. A performance comparison with the state-of-the-art deep learning methods and image-based spectrogram approach was conducted.

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