Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 35, Issue 3, Pages 2362-2373Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2019.2953225
Keywords
Task analysis; Home appliances; Aggregates; Power demand; Feature extraction; Monitoring; Hidden Markov models; Non-intrusive load monitoring (NILM); convolutional neural network; self-attention; generative adversarial network; energy disaggregation
Categories
Funding
- 2019 Seed Fund Award from CITRIS
- Banatao Institute at the University of California
- Hellman Fellowship
Ask authors/readers for more resources
Non-intrusive load monitoring addresses the challenging task of decomposing the aggregate signal of a household's electricity consumption into appliance-level data without installing dedicated meters. By detecting load malfunction and recommending energy reduction programs, cost-effective non-intrusive load monitoring provides intelligent demand-side management for utilities and end users. In this paper, we boost the accuracy of energy disaggregation with a novel neural network structure named scale- and context-aware network, which exploits multi-scale features and contextual information. Specifically, we develop a multi-branch architecture with multiple receptive field sizes and branch-wise gates that connect the branches in the sub-networks. We build a self-attention module to facilitate the integration of global context, and we incorporate an adversarial loss and on-state augmentation to further improve the model's performance. Extensive simulation results tested on open datasets corroborate the merits of the proposed approach, which significantly outperforms state-of-the-art methods.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available