Sequence-to-Point Learning With Neural Networks for Non-Intrusive Load Monitoring

Authors

  • Chaoyun Zhang University of Edinburgh
  • Mingjun Zhong University of Lincoln
  • Zongzuo Wang University of Edinburgh
  • Nigel Goddard University of Edinburgh
  • Charles Sutton University of Edinburgh

Keywords:

MLA: Applications of Supervised Learning, ML: Deep Learning/Neural Networks, NILM, Disagregation, Single Channel Blind Source Separation

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%.

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Published

2018-04-26

How to Cite

Zhang, C., Zhong, M., Wang, Z., Goddard, N., & Sutton, C. (2018). Sequence-to-Point Learning With Neural Networks for Non-Intrusive Load Monitoring. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11873

Issue

Section

Main Track: Machine Learning Applications