Deep Neural Network Implementation for Appliances Identification by Using Current and Voltage Signatures

Yılmaz Güven

doi:10.5152/tepes.2024.24030

Deep Neural Network Implementation for Appliances Identification by Using Current and Voltage Signatures

Abstract

The PLAID database was published earlier last year and contains many households’ devices and appliances with numerous high-frequency measurements at different locations. In this paper, the researchers extracted features such as peak to peak, peak to root mean square, minimum, maximum, mean, median value, standard deviation, phase angle, active and reactive power from these current and voltage signals. A multi-layered and SoftMax back-propagated artificial deep neural network (DNN) has been trained and tested with these data. Batch normalization has been used to optimize the DNN. Different architectures, activation functions, and training algorithms have been tried out to get the best results. Then this method was implemented within a low-cost embedded system to identify appliances by using their current and voltage signature. This device provides an identification method using only one sensor within an embedded system, and accuracy of the DNN is slightly better than studies which use the same dataset. On the other hand, deploying trained neural networks on an embedded system can be tricky and overwhelming. This paper also demonstrated that using open standards for machine learning makes these processes and gives interoperability.

Keywords

Thanks

This study has been conducted with personal funds, and facility of Kırklareli University have been used.

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Details

Primary Language

English

Subjects

Electrical Engineering (Other)

Journal Section

Research Article

Authors

Yılmaz Güven ^*
0000-0002-8205-2490
Türkiye

Publication Date

February 28, 2025

Submission Date

September 26, 2024

Acceptance Date

October 15, 2024

Published in Issue

Year 2025 Volume: 5 Number: 1

DOI

https://doi.org/10.5152/tepes.2024.24030

IZ

https://izlik.org/JA83AS27FN

Cite

RIS / Bibtex

APA

Güven, Y. (2025). Deep Neural Network Implementation for Appliances Identification by Using Current and Voltage Signatures. Turkish Journal of Electrical Power and Energy Systems, 5(1), 19-29. https://doi.org/10.5152/tepes.2024.24030

AMA

1.Güven Y. Deep Neural Network Implementation for Appliances Identification by Using Current and Voltage Signatures. TEPES. 2025;5(1):19-29. doi:10.5152/tepes.2024.24030

Chicago

Güven, Yılmaz. 2025. “Deep Neural Network Implementation for Appliances Identification by Using Current and Voltage Signatures”. Turkish Journal of Electrical Power and Energy Systems 5 (1): 19-29. https://doi.org/10.5152/tepes.2024.24030.

EndNote

Güven Y (February 1, 2025) Deep Neural Network Implementation for Appliances Identification by Using Current and Voltage Signatures. Turkish Journal of Electrical Power and Energy Systems 5 1 19–29.

IEEE

[1]Y. Güven, “Deep Neural Network Implementation for Appliances Identification by Using Current and Voltage Signatures”, TEPES, vol. 5, no. 1, pp. 19–29, Feb. 2025, doi: 10.5152/tepes.2024.24030.

ISNAD

Güven, Yılmaz. “Deep Neural Network Implementation for Appliances Identification by Using Current and Voltage Signatures”. Turkish Journal of Electrical Power and Energy Systems 5/1 (February 1, 2025): 19-29. https://doi.org/10.5152/tepes.2024.24030.

JAMA

1.Güven Y. Deep Neural Network Implementation for Appliances Identification by Using Current and Voltage Signatures. TEPES. 2025;5:19–29.

MLA

Güven, Yılmaz. “Deep Neural Network Implementation for Appliances Identification by Using Current and Voltage Signatures”. Turkish Journal of Electrical Power and Energy Systems, vol. 5, no. 1, Feb. 2025, pp. 19-29, doi:10.5152/tepes.2024.24030.

Vancouver

1.Yılmaz Güven. Deep Neural Network Implementation for Appliances Identification by Using Current and Voltage Signatures. TEPES. 2025 Feb. 1;5(1):19-2. doi:10.5152/tepes.2024.24030