Image-based mains signal disaggregation and load recognition

Liston Matindife, Yanxia Sun, Zenghui Wang

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

The mains signal is a complex fusion of various electrical equipment load signals in a building. In the non-intrusive load monitoring recognition, our main aim is to be able to extract as much load features as possible from the complex aggregate mains signal in a simpler way through a computer vision-based approach as opposed to the powers series signal approach. Power series methods, which are one dimensional in nature, suffer from poor aggregate and load signal feature localization necessitating a larger training dataset spanning very long time periods and normally require signal formatting and pre-processing. We use Gramian angular summation fields to transform the power series into a reduced image dataset that contains a rich set of localized signal features. A computer vision approach allows us to capture as much information as possible, and then propose an image-based mains load recognition system with high performance. In this paper for the entire recognition system, we use convolutional neural networks that very well adapted to vision recognition. The load signal image disaggregation is achieved through the powerful stacked denoising autoencoder noise extraction network. To test the proposed system, some simulations and comparisons are carried out and the results show that our easier to handle method can achieve acceptable performance.

Original languageEnglish
Pages (from-to)901-927
Number of pages27
JournalComplex and Intelligent Systems
Volume7
Issue number2
DOIs
Publication statusPublished - Apr 2021

Keywords

  • Convolutional neural network
  • Image disaggregation
  • Image recognition
  • Mains supply load recognition
  • Stacked denoising autoencoder

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Engineering (miscellaneous)
  • Computational Mathematics

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