Temporal Patternization of Power Signatures for Appliance Classification in NILM

Hwan Kim and Sungsu Lim
Additional contact information
Hwan Kim: Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Korea
Sungsu Lim: Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Korea

Energies, 2021, vol. 14, issue 10, 1-17

Abstract: 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.

Keywords: non-intrusive load monitoring (NILM); load identification; convolutional neural network (CNN); deep learning; temporal bar graph; temporal patternization (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/10/2931/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/10/2931/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:10:p:2931-:d:557516

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().