Scale- and Context-Aware Convolutional Non-intrusive Load Monitoring

Personal use of this material is permitted. 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 malfunctio n and recommending energy reduction programs, cost-effective n on-intrusive load monitoring provides intelligent demand-si de 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 inform ation. Specifically, we develop a multi-branch architecture with m ultiple receptive field sizes and branch-wise gates that connect the branches in the sub-networks. We build a self-attention mod ule to facilitate the integration of global context, and we inco rporate an adversarial loss and on-state augmentation to further im prove the model's performance. Extensive simulation results tes ted on open datasets corroborate the merits of the proposed approa ch, which significantly outperforms state-of-the-art methods . Non-intrusive load monitoring (NILM) is the task of estimating the power demand of a specific appliance from the aggregate consumption of a household measured by a single meter [1]. As the task requires breaking down the total energ y consumed by multiple appliances into appliance-level ener gy consumption records, NILM is synonymous with the phrase "energy disaggregation" [2]. A direct benefit of NILM is that energy end-users can acquire appliance-level consump tion feedbacks and optimize their energy consumption behaviour s accordingly. It is estimated that up to 12% residential ener gy saving can be achieved by providing appliance-level feedba ck [3].