Disaggregated power system signal recognition using capsule network

Liston Matindife, Yanxia Sun, Zenghui Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


Non-intrusive-load-monitoring (NILM) is normally based on power series analysis. In the load classification stage we use an image based deep convolutional neural network (DCNN) which is modelled on the biological visual cortex thereby achieving extremely high levels of object recognition and classification. However, the downsize to the DCNN is the requirement of a large image training dataset, translational invariance and loss during max pooling of information captured in small signal perturbations. In this paper to reduce the training dataset, provide appliance signature equivariance recognition and replace max pooling with routing by agreement for improved NILM recognition and classification we use Hinton’s capsule network (CapsNet). Disaggregated appliance current, real power and power factor signals are converted to two-dimensional (2D) images and then complementary fused together for increased recognition accuracy before final input into the CapsNet. We implement the Discrete Wavelet Transform (DWT) since it is able to transform within a large frequency band portfolio and fuses well low pixel images. By using image fusion technique we show that for only fifteen images per appliance and with no data augmentation we are able to achieve average prediction accuracies of up to 93.75% and hence consolidate the validity of the CapsNet in the NILM recognition and classification scheme for limited data memory and improved recognition.

Original languageEnglish
Title of host publicationNeural Computing for Advanced Applications - 1st International Conference, NCAA 2020, Proceedings
EditorsHaijun Zhang, Zhao Zhang, Zhou Wu, Tianyong Hao
Number of pages12
ISBN (Print)9789811576690
Publication statusPublished - 2020
Event1st International Conference on Neural Computing for Advanced Applications, NCAA 2020 - Shenzhen, China
Duration: 3 Jul 20205 Jul 2020

Publication series

NameCommunications in Computer and Information Science
Volume1265 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference1st International Conference on Neural Computing for Advanced Applications, NCAA 2020


  • Capsule network
  • Deep learning
  • Disaggregated power systems signal
  • Image fusion
  • Non-intrusive-load-monitoring
  • Signal to 2D image transformations

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics


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