Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks

Hilal Yıldız; Musa Balta

doi:10.35377/saucis.8.94717.1641393

EN

Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks

Abstract

The problems of traditional electricity grids have led to the emergence of smart grids. Unlike traditional energy systems, smart grids play an important role in the energy sector with their flexibility, programmability and reliability. However, the heterogeneous structure of smart grids consisting of different devices and protocols poses some problems in terms of complexity, service quality and security. In the literature, SDN (Software Defined Networks) paradigm is proposed as a solution to these problems. SDN and smart grid integration makes the energy sector more efficient, reliable and sustainable. On the other hand, smart meters used in the consumption area of smart grids provide instantaneous transmission of energy production and consumption data in homes to the center. With the support of IoT (Internet of Things) of these meters and components in the home area network (oven, IP camera, TV, etc.), the energy supply and demand balance can be managed more smoothly. In this study, a software-defined and IoT-based smart home architecture is proposed to obtain real energy consumption data. The proposed architecture is developed and implemented on the Mininet simulator with python code. As a result of simulations run under different process and attack scenarios, energy consumption data sets were created. A comparison of the anomaly detection performances of machine learning algorithms on the data sets that are considered to contribute to the literature has been made. As a result of this comparison, it was observed that the success rate of the random forest algorithm was higher than the other algorithms with 90-95 percent.

Keywords

Supporting Institution

Sakarya University, Scientific Research Projects Unit

Project Number

2022-6-23-68

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Details

Primary Language

English

Subjects

Computer Software , Software Engineering (Other)

Journal Section

Research Article

Authors

Hilal Yıldız
0000-0001-6840-8545
Türkiye

Musa Balta ^*
0000-0002-8711-6625
Türkiye

Early Pub Date

September 29, 2025

Publication Date

September 30, 2025

Submission Date

February 17, 2025

Acceptance Date

September 17, 2025

Published in Issue

Year 2025 Volume: 8 Number: 3

DOI

https://doi.org/10.35377/saucis.8.94717.1641393

IZ

https://izlik.org/JA58XB22GZ

Cite

RIS / Bibtex

APA

Yıldız, H., & Balta, M. (2025). Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks. Sakarya University Journal of Computer and Information Sciences, 8(3), 518-535. https://doi.org/10.35377/saucis.8.94717.1641393

AMA

1.Yıldız H, Balta M. Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks. SAUCIS. 2025;8(3):518-535. doi:10.35377/saucis.8.94717.1641393

Chicago

Yıldız, Hilal, and Musa Balta. 2025. “Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks”. Sakarya University Journal of Computer and Information Sciences 8 (3): 518-35. https://doi.org/10.35377/saucis.8.94717.1641393.

EndNote

Yıldız H, Balta M (September 1, 2025) Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks. Sakarya University Journal of Computer and Information Sciences 8 3 518–535.

IEEE

[1]H. Yıldız and M. Balta, “Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks”, SAUCIS, vol. 8, no. 3, pp. 518–535, Sept. 2025, doi: 10.35377/saucis.8.94717.1641393.

ISNAD

Yıldız, Hilal - Balta, Musa. “Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks”. Sakarya University Journal of Computer and Information Sciences 8/3 (September 1, 2025): 518-535. https://doi.org/10.35377/saucis.8.94717.1641393.

JAMA

1.Yıldız H, Balta M. Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks. SAUCIS. 2025;8:518–535.

MLA

Yıldız, Hilal, and Musa Balta. “Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 3, Sept. 2025, pp. 518-35, doi:10.35377/saucis.8.94717.1641393.

Vancouver

1.Hilal Yıldız, Musa Balta. Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks. SAUCIS. 2025 Sep. 1;8(3):518-35. doi:10.35377/saucis.8.94717.1641393

Öz

Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks

Abstract

Keywords

Supporting Institution

Project Number

References

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Early Pub Date

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Published in Issue

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