Scale- and Context-Aware Convolutional Non-intrusive Load Monitoring
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- November 2019
- DOI:
- 10.48550/arXiv.1911.07183
- arXiv:
- arXiv:1911.07183
- Bibcode:
- 2019arXiv191107183C
- Keywords:
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- Electrical Engineering and Systems Science - Signal Processing;
- Computer Science - Machine Learning;
- Statistics - Machine Learning
- E-Print:
- Accepted by IEEE Transactions on Power Systems