Paper Info
Title
Semi-Supervised Domain Adaptation for Multi-Label Classification on Nonintrusive Load Monitoring
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-student 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.
Year
DOI
Venue
2022
10.3390/s22155838
SENSORS
Keywords
DocType
Volume
nonintrusive load monitoring, transfer learning, domain adaptation, pseudo labeling, semi-supervised learning, appliance usage classification
Journal
22
Issue
ISSN
Citations 
15
1424-8220
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Cheonghwan Hur111.71
Han-Eum Lee200.34
Young-Joo Kim300.68
Sang-Gil Kang400.34