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).

提出了一种利用深度学习解决非侵入式负载监测（NILM）问题的方法，包括解决家用和场地级别的 NILM 问题以及家电识别等相关问题，利用低频 REED 数据集和 REFIT 数据集对模型进行训练和测试，能够达到最高 94.6％ 的准确率，为实现节能方案提供参考。