Event-based S-transform approach for nonintrusive load monitoring

Khairuddin Khalid, Azah Mohamed, Hussain Shareef, Maytham Sabeeh

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


In this study, a nonintrusive load monitoring system is developed by analyzing the power signal obtained from a single point of power meter installation to detect ON/OFF load activities. A mathematically designed model with backpropagation neural network is utilized in load pattern recognition to decompose the load operation. Leveraging its unique load signature profile, the S-transform approach is employed to extract the features from the aggregate power signal and analyze the detection of load start-up transient from signal processing. To improve the accuracy of load identification for unknown data, the power factor is used as an additive feature with 99.32% load recognition accuracy.

Original languageEnglish
Pages (from-to)194-198
Number of pages5
JournalPrzeglad Elektrotechniczny
Issue number5
Publication statusPublished - Apr 1 2016
Externally publishedYes


  • Artificial neural network
  • Feature extraction
  • Load recognition
  • S-transform

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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