Real-Time Appliances Recognition for Non-Intrusive Load Monitoring Using Convolutional Neural Networks

Authors

  • Luai Saeed M. Saif Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia.
  • Yewguan Soo Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia.
  • Kim-Chuan Lim Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia.
  • Zulkalnain Mohd Yussof Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia.
  • Nurulfajar Abd Manap Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia.
  • Yih-Hwa Ho Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia.
  • Ranjit Singh Sarban Singh Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia.
  • Sani Irwan Md Salim Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia.
  • Feng Duan College of Information Technical Science, Nankai University, Tianjin, China

Keywords:

Convolutional Neural Networks (CNN), Current Sensor (CT), Envelope Signal, Non-intrusive Load Monitoring (NILM), Power Factor (PF), Root Mean Square (RMS), Spectrogram,

Abstract

Up to now, the details of the load-level power consumption are generally not available to the customers who wish to get more information about their power usage. This paper shows the result of using Convolutional Neural Networks (CNN) to recognize the type of any electrical appliance while operating as well as its power consumption. This approach allows the monitoring on a loads power consumption on every electrical appliance individually. By applying an envelope function to the signal, the appliance can be recognized successfully even it only consumes a small amount of energy during its operation. The performance was evaluated on three electrical appliances at different power consumption level.

Downloads

Download data is not yet available.

Downloads

Published

2018-07-04

How to Cite

M. Saif, L. S., Soo, Y., Lim, K.-C., Mohd Yussof, Z., Abd Manap, N., Ho, Y.-H., Sarban Singh, R. S., Md Salim, S. I., & Duan, F. (2018). Real-Time Appliances Recognition for Non-Intrusive Load Monitoring Using Convolutional Neural Networks. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-5), 35–38. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4346

Issue

Vol. 10 No. 2-5: Embracing The 4th Industrial Revolution: Innovation in Telecommunication, Electronic & Computer Engineering I

Section

Articles

License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

Most read articles by the same author(s)

1 2 > >>