Real-Time Appliances Recognition for Non-Intrusive Load Monitoring Using Convolutional Neural Networks
Keywords:Convolutional Neural Networks (CNN), Current Sensor (CT), Envelope Signal, Non-intrusive Load Monitoring (NILM), Power Factor (PF), Root Mean Square (RMS), Spectrogram,
AbstractUp 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.
How to Cite
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.