  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 state-of-the-art (pre-trained)
2D-CNN models, i.e., AlexNet, ResNet-18, and DenseNet-121, which are fine-tuned
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 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).
