Sequence-to-point learning with neural networks for nonintrusive load monitoring

Zhang, Chaoyun and Zhong, Mingjun and Wang, Zongzuo and Goddard, Nigel and Sutton, Charles (2018) Sequence-to-point learning with neural networks for nonintrusive load monitoring. In: The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), 2 - 7 February 2018, New Orleans.

Documents
1612.09106.pdf
[img]
[Download]
[img]
Preview
PDF
1612.09106.pdf - Whole Document

613kB
Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

Energy disaggregation (a.k.a nonintrusive load monitoring, NILM), a single-channel blind source separation problem, aims to decompose the mains which records the whole house electricity consumption into appliance-wise readings. This problem is difficult because it is inherently unidentifiable. Recent approaches have shown that the identifiability problem could be reduced by introducing domain knowledge into the model. Deep neural networks have been shown to be a promising approach for these problems, but sliding windows are necessary to handle the long sequences which arise in signal processing problems, which raises issues about how to
combine predictions from different sliding windows. In this paper, we propose sequence-to-point learning, where the input is a window of the mains and the output is a single point of the target appliance. We use convolutional neural networks to train the model. Interestingly, we systematically show that the convolutional neural networks can inherently learn the signatures of the target appliances, which are automatically added into the model to reduce the identifiability problem. We applied
the proposed neural network approaches to real-world household energy data, and show that the methods achieve state-of-the-art performance, improving two standard error measures by 84% and 92%.

Keywords:Deep learning; single-channel blind source separation; nonintrusive load monitoing;
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > School of Computer Science
Related URLs:
ID Code:29991
Deposited On:14 Dec 2017 14:31

Repository Staff Only: item control page