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Identification of electrical appliances using non-intrusive magnetic field and Probabilistic Neural Network (PNN)

The electricity waste is severe especially in large organizational buildings where the use of air conditioners, fridges and electrical motors are rampant. Due to lack of energy saving consciousness, users may not switch off this equipment after use. Thus, it would be an advantage if there exist a sy...

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Main Authors: Mohd Rosdi N.A., Nordin F.H., Ramasamy A.K.
Other Authors: 56602822900
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-219112023-05-16T10:46:01Z Identification of electrical appliances using non-intrusive magnetic field and Probabilistic Neural Network (PNN) Mohd Rosdi N.A. Nordin F.H. Ramasamy A.K. 56602822900 25930510500 16023154400 The electricity waste is severe especially in large organizational buildings where the use of air conditioners, fridges and electrical motors are rampant. Due to lack of energy saving consciousness, users may not switch off this equipment after use. Thus, it would be an advantage if there exist a system that will be able to identify the appliances from one place without the residence having to go and check the state of the appliance or without having to place various sensors intrusively. Since most electrical appliances emit magnetic fields, the paper proposes to use non-intrusive magnetic field signature waveforms to identify the type of appliance used. The magnetic field emitted by table fan, blender and hairdryer are chosen for this purpose. The magnetic field from these three appliances are collected from four different measurement distances i.e. (i) 0cm (ii) 10cm (iii) 30cm and (iv) 60cm. The features of the magnetic field are then extracted and trained offline using the Probabilistic Neural Network (PNN). Once trained, the PNN shows that it is able to successfully identify the appliances regardless of the measurement distance. © 2014 IEEE. Final 2023-05-16T02:46:01Z 2023-05-16T02:46:01Z 2014 Conference Paper 10.1109/PECON.2014.7062412 2-s2.0-84946690076 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946690076&doi=10.1109%2fPECON.2014.7062412&partnerID=40&md5=f1888a1c2115fdfc5e4ffae9644cf582 https://irepository.uniten.edu.my/handle/123456789/21911 7062412 47 52 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description The electricity waste is severe especially in large organizational buildings where the use of air conditioners, fridges and electrical motors are rampant. Due to lack of energy saving consciousness, users may not switch off this equipment after use. Thus, it would be an advantage if there exist a system that will be able to identify the appliances from one place without the residence having to go and check the state of the appliance or without having to place various sensors intrusively. Since most electrical appliances emit magnetic fields, the paper proposes to use non-intrusive magnetic field signature waveforms to identify the type of appliance used. The magnetic field emitted by table fan, blender and hairdryer are chosen for this purpose. The magnetic field from these three appliances are collected from four different measurement distances i.e. (i) 0cm (ii) 10cm (iii) 30cm and (iv) 60cm. The features of the magnetic field are then extracted and trained offline using the Probabilistic Neural Network (PNN). Once trained, the PNN shows that it is able to successfully identify the appliances regardless of the measurement distance. © 2014 IEEE.
author2 56602822900
author_facet 56602822900
Mohd Rosdi N.A.
Nordin F.H.
Ramasamy A.K.
format Conference Paper
author Mohd Rosdi N.A.
Nordin F.H.
Ramasamy A.K.
spellingShingle Mohd Rosdi N.A.
Nordin F.H.
Ramasamy A.K.
Identification of electrical appliances using non-intrusive magnetic field and Probabilistic Neural Network (PNN)
author_sort Mohd Rosdi N.A.
title Identification of electrical appliances using non-intrusive magnetic field and Probabilistic Neural Network (PNN)
title_short Identification of electrical appliances using non-intrusive magnetic field and Probabilistic Neural Network (PNN)
title_full Identification of electrical appliances using non-intrusive magnetic field and Probabilistic Neural Network (PNN)
title_fullStr Identification of electrical appliances using non-intrusive magnetic field and Probabilistic Neural Network (PNN)
title_full_unstemmed Identification of electrical appliances using non-intrusive magnetic field and Probabilistic Neural Network (PNN)
title_sort identification of electrical appliances using non-intrusive magnetic field and probabilistic neural network (pnn)
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806428354526576640
score 13.222552

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