期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 16, 期 11, 页码 6892-6902出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2019.2955470
关键词
Deep learning; Feature extraction; Semisupervised learning; Monitoring; Informatics; Hidden Markov models; Transforms; Deep learning; load signatures; multilabel classification; nonintrusive load monitoring (NILM); semisupervised learning (SSL)
类别
资金
- National Natural Science Foundation of China [61427801, 61601040]
- BUPT Excellent Ph.D.
- Students Foundation [CX2018204]
- China Scholarship Council
Nonintrusive load monitoring (NILM) is a technique that infers appliance-level energy consumption patterns and operation state changes based on feeder power signals. With the availability of fine-grained electric load profiles, there has been increasing interest in using this approach for demand-side energy management in smart grids. NILM is a multilabel classification problem due to the simultaneous operation of multiple appliances. Recently, deep learning based techniques have been shown to be a promising approach to solving this problem, but annotating the huge volume of load profile data with multiple active appliances for learning is very challenging and impractical. In this article, a new semisupervised multilabel deep learning based framework is proposed to address this problem with the goal of mitigating the reliance on large labeled datasets. Specifically, a temporal convolutional neural network is used to automatically extract high-level load signatures for individual appliances. These signatures can be efficiently used to improve the feature representation capability of the framework. Case studies conducted on two open-access NILM datasets demonstrate the effectiveness and superiority of the proposed approach.
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