Non-Intrusive Load Monitoring (NILM) seeks to save energy by estimating individual appliance power usage from a single aggregate measurement. Deep neural networks have become increasingly popular in attempting to solve NILM problems. However most used models are used for Load Identification rather than online Source Separation. Among source separation models, most use a single-task learning approach in which a neural network is trained exclusively for each appliance. This strategy is computationally expensive and ignores the fact that multiple appliances can be active simultaneously and dependencies between them. The rest of models are not causal, which is important for real-time application. Inspired by Convtas-Net, a model for speech separation, we propose Conv-NILM-net, a fully convolutional framework for end-to-end NILM. Conv-NILM-net is a causal model for multi appliance source separation. Our model is tested on two real datasets REDD and UK-DALE and clearly outperforms the state of the art while keeping a significantly smaller size than the competing models.

Non-intrusive load monitoring (NILM) or energy disaggregation is an inverse problem whereby the goal is to extract the load profiles of individual appliances, given an aggregate load profile of the mains of a home. NILM could help identify the power usage patterns of individual appliances in a home, and thus, could help realize novel energy conservation schemes for smart homes. In this backdrop, this work proposes a novel deep-learning approach to solve the NILM problem and a few related problems as follows. 1) We build upon the reputed seq2-point convolutional neural network (CNN) model to come up with the proposed seq2-[3]-point CNN model to solve the (home) NILM problem and site-NILM problem (basically, NILM at a smaller scale). 2) We solve the related problem of appliance identification by building upon the state-of-the-art (pre-trained) 2D-CNN models, i.e., AlexNet, ResNet-18, and DenseNet-121, which are trained upon two custom datasets that consist of Wavelets and short-time Fourier transform (STFT)-based 2D electrical signatures of the appliances. 3) Finally, we do some basic qualitative inference about an individual appliance's health by comparing the power consumption of the same appliance across multiple homes. Low-frequency REDD dataset is used to train and test the proposed deep learning models for all problems, except site-NILM where REFIT dataset has been used. As for the results, we achieve a maximum accuracy of 94.6\% for home-NILM, 81\% for site-NILM, and 88.9\% for appliance identification (with Resnet-based model).

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].