  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.
