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학술지 Semi-Supervised Domain Adaptation for Multi-Label Classification on Nonintrusive Load Monitoring
Cited 8 time in scopus Download 64 time Share share facebook twitter linkedin kakaostory
저자
허청환, 이한음, 김영주, 강상길
발행일
202208
출처
Sensors, v.22 no.15, pp.1-20
ISSN
1424-8220
출판사
MDPI
DOI
https://dx.doi.org/10.3390/s22155838
협약과제
22ZS1300, 인공지능 처리성능 한계를 극복하는 고성능 컴퓨팅 기술 연구, 김강호
초록
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 제안 키워드
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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