Energy Disaggregation & Appliance Identification in a Smart Home: Transfer Learning Enables Edge Computing

Non-intrusive load monitoring (NILM) or energy disaggregation aims to extract the load profiles of individual consumer electronic appliances, given an aggregate load profile of the mains of a smart home. This work proposes a novel deep-learning and edge computing 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 pre-trained 2D-CNN models, i.e., AlexNet, ResNet-18, and DenseNet-121, which are fine-tuned on two custom datasets that consist of Wavelets and short-time Fourier transform (STFT)-based 2D electrical signatures of the appliances. Low-frequency REDD dataset is used for all problems, except site-NILM where REFIT dataset is 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 model).