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Journal Article Semi-Supervised Domain Adaptation for Multi-Label Classification on Nonintrusive Load Monitoring
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Authors
Cheong-Hwan Hur, Han-Eum Lee, Young-Joo Kim, Sang-Gil Kang
Issue Date
2022-08
Citation
Sensors, v.22, no.15, pp.1-20
ISSN
1424-8220
Publisher
MDPI
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.3390/s22155838
Project Code
22ZS1300, Research on High Performance Computing Technology to overcome limitations of AI processing, Kim Kang Ho
Abstract
Nonintrusive load monitoring (NILM) is a technology that analyzes the load consumption and usage of an appliance from the total load. NILM is becoming increasingly important because residential and commercial power consumption account for about 60% of global energy consumption. Deep neural network-based NILM studies have increased rapidly as hardware computation costs have decreased. A significant amount of labeled data is required to train deep neural networks. However, installing smart meters on each appliance of all households for data collection requires the cost of geometric series. Therefore, it is urgent to detect whether the appliance is used from the total load without installing a separate smart meter. In other words, domain adaptation research, which can interpret the huge complexity of data and generalize information from various environments, has become a major challenge for NILM. In this research, we optimize domain adaptation by employing techniques such as robust knowledge distillation based on teacher?뱒tudent structure, reduced complexity of feature distribution based on gkMMD, TCN-based feature extraction, and pseudo-labeling-based domain stabilization. In the experiments, we down-sample the UK-DALE and REDD datasets as in the real environment, and then verify the proposed model in various cases and discuss the results.
KSP Keywords
Data Collection, Deep neural network(DNN), Domain stabilization, Feature extractioN, Global Energy, Labeled data, Load consumption, Nonintrusive Load Monitoring(NILM), Power Consumption, Proposed model, Real environment
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