Neural Computing and Applications

, Volume 31, Issue 12, pp 8931–8953 | Cite as

Dilated residual attention network for load disaggregation

  • Min XiaEmail author
  • Wan’an Liu
  • Yiqing Xu
  • Ke Wang
  • Xu Zhang
Original Article


Load disaggregation technology is a key technology to realize real-time nonintrusive load monitoring (NILM), and deep learning method has shown great promise for NILM. However, current load disaggregation models based on deep learning are prone to the problems of gradient disappearance and model degradation, and it is difficult to extract effective features from load time series. In order to solve these problems, a new dilated residual attention deep network is proposed for load disaggregation. The proposed model adopts residual learning to extract high-level load features, reduces the difficulty of network optimization and solves the problem of network gradient disappearance. Dilated convolution is introduced to increase the receptive field of convolution kernels, which solves the problem that long-load time-series data are difficult to be learned. Most important of all, the proposed bottom-up and top-down attention mechanism can effectively extract the features of the abrupt points in mains power, improve the accuracy of judging the on/off state of electrical appliances and at the same time improve the learning ability of electrical appliances with low usage. Experiments on WikiEnergy dataset and UK-DALE dataset show that the proposed method achieves more accurate load disaggregation tasks than existing studies, which is of great significance for realizing practical NILM.


Load disaggregation Dilated convolution Residual learning Attention mechanism 



This work is supported in part by the National Natural Science Foundation of PR China (61773219), the Natural Science Foundation of Jiangsu (BK20161533), State Grid Corporation of China Project ‘Fundamental Theory of Dynamic Demand Response Control Based on Large-Scale Diversified Demand Side Resources.’

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment TechnologyNanjing University of Information Science and TechnologyNanjingChina
  2. 2.College of Information Science and TechnologyNanjing Forestry UniversityNanjingChina
  3. 3.China Electric Power Research InstituteNanjingChina

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