期刊
IEEE TRANSACTIONS ON SMART GRID
卷 10, 期 3, 页码 2969-2978出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2018.2815763
关键词
Deep learning; energy disaggregation; multi-label classification; non-intrusive load monitoring
资金
- Infosys Center for Artifical Intelligence at IIIT Delhi
- DST IC-IMPACTS Indo-Canadian Grant
Currently there are several well-known approaches to non-intrusive appliance load monitoring-rule based, stochastic finite state machines, neural networks, and sparse coding. Recently several studies have proposed a new approach based on multi-label classification. Different appliances are treated as separate classes, and the task is to identify the classes given the aggregate smart-meter reading. Prior studies in this area have used off-the-shelf algorithms like multi label K nearest neighbor and random K-label sets to address this problem. In this paper, we propose a deep learning-based technique. There are hardly any studies in deep learning based multi-label classification; two new deep learning techniques to solve the said problem are fundamental contributions of this paper. These are deep dictionary learning and deep transform learning. Thorough experimental results on benchmark datasets show marked improvement over existing studies.
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