004.7:004.8:621.3

energy networks, energy efficiency, communication protocols, machine learning methods, adaptive  protocol selection.

This paper examines approaches to implementing adaptive multi-protocol communication in energy systems undergoing transformation in the context of distributed generation growth and Smart Grid concept development. An architecture is proposed that integrates OpenID Connect (a unified authentication provider) with a machine learning module for dynamic selection of optimal data transmission protocols among MQTT, CoAP, HTTPS protocols and legacy systems. The solution is based on employing widely-used algorithms (Random Forest, neural networks, logistic regression) for real-time communication efficiency prediction. The system ensures flexible, secure, and scalable management of heterogeneous devices through a unified control center. The obtained results demonstrate potential for communication cost reduction, reliability enhancement, and foundation establishment for implementing intelligent communication systems in the energy sector with automatic protocol switching.

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7. Chen X., McElroy M. B., Wu Q., Shu Y., & Xue Y. (2019). Transition towards higher penetration of renewables: an overview of interlinked technical, environmental and socio-economic challenges. Journal of Modern Power Systems and Clean Energy, 7(1), 1–18. https://doi.org/10.1007/s40565-018-0438-9.
8. Starchenko V. (2021) Traffic optimization in wifi networks for the internet of things. Scientific Journal of the Ternopil National Technical University, 104 (4), 131–142. https://doi.org/10.33108/visnyk_tntu2021. 04.131.
9. Naik N. (2017). Choice of effective messaging protocols for IoT systems: MQTT, CoAP, AMQP and HTTP. In 2017 IEEE International Systems Engineering Symposium (ISSE) (pp. 1–7). IEEE. https://doi.org/10.1109/syseng.2017.8088251.
10. Al-Masri, E., et al. (2020). Investigating messaging protocols for the Internet of Things (IoT). IEEE Access, 8, 94880-94911. https://doi.org/10.1109/ACCESS.2020.2993363.
11. Thangavel D., Ma X., Valera A., Tan H.-X., & Tan C. K.-Y. (2014). Performance evaluation of MQTT and CoAP via a common middleware. In 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE. https://doi.org/10.1109/issnip.2014. 6827678.
12. Rescorla E. (2018). The Transport Layer Security (TLS) Protocol Version 1.3. RFC 8446. Internet Engineering Task Force. https://doi.org/10.17487/RFC8446.
13. Kumar S., & Patel D. R. (2019) A survey on Internet of Things: Security and privacy issues. International Journal of Computer Applications, 90 (11), 20–26. https://doi.org/10.5120/15764-4454.
14. Hardt D. (2020). The OAuth 2.0 Authorization Framework. RFC 6749 (Updated). Internet Engineering Task Force. https://doi.org/10.17487/RFC6749.
15. Jones M., et al. (2019). OpenID Connect Core 1.0 incorporating errata set 1. OpenID Foundation. https://openid.net/specs/openid-connect-core-1_0.html.
16. Martsenyuk V., & Kit N. (2024) A multivariate method of forecasting the nonlinear dynamics of production network based on multilayer neural models. Scientific Journal of the Ternopil National Technical University, 114 (2), 39–50. https://doi.org/10.33108/visnyk_tntu2024.02.039.
17. Combs G., et al. (2023). Wireshark User's Guide for Wireshark 4.0. Wireshark Foundation. https://www. wireshark.org/docs/wsug_html/.
18. Raschka S. (2018). Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning (Version 3). arXiv. https://doi.org/10.48550/ARXIV.1811.12808.
19. International Electrotechnical Commission. (2018). IEC 61850-5-2018: Communication requirements for functions and device models. IEC Publications.
20. IEEE Standards Association. (2018). IEEE Standard 1613-2018: Environmental and Testing Requirements for Communications Networking Devices in Electric Power Substations. IEEE.
21. A. Voloshchuk, D. Velychko, H. Osukhivska, A. Palamar, (2024). Computer system for energy distribution in conditions of electricity shortage using artificial intelligence, in: Proceedings of the 2nd International Workshop on Computer Information Technologies in Industry 4.0 (CITI 2024), Volume 3742, Ternopil, Ukraine, June 12–14, pp. 66–75. Available at: https://ceur-ws.org/Vol-3742/paper5.pdf.

1. International Energy Agency. (2023). World Energy Outlook 2023. IEA Publications. Available at:  https: //www.iea.org/reports/world-energy-outlook-2023.
2. IoT Analytics. (2024). State of IoT Summer 2024. Available at: https://iot-analytics.com/number-connected -iot-devices/.
3. Ahmad T., Chen H., Guo Y., & Wang J. (2018). A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand. Energy and Buildings, 165, 301–320. https://doi.org/10.1016/j.enbuild.2018.01.017.
4. Zhang Y., Wang J., & Wang X. (2020). Review on probabilistic forecasting of wind power generation with machine learning. Renewable and Sustainable Energy Reviews, 125, 109827. https://doi.org/10.1016/ j.rser.2020.109827.
5. Dileep G. (2020). A survey on smart grid technologies and applications. Renewable Energy, 146, 2589–2625. https://doi.org/10.1016/j.renene.2019.08.092.
6. Kabalci Y. (2016). A survey on smart metering and smart grid communication. Renewable and Sustainable Energy Reviews, 57, 302–318. https://doi.org/10.1016/j.rser.2015.12.114.
7. Chen X., McElroy M. B., Wu Q., Shu Y., & Xue Y. (2019). Transition towards higher penetration of renewables: an overview of interlinked technical, environmental and socio-economic challenges. Journal of Modern Power Systems and Clean Energy, 7(1), 1–18. https://doi.org/10.1007/s40565-018-0438-9.
8. Starchenko V. (2021) Traffic optimization in wifi networks for the internet of things. Scientific Journal of the Ternopil National Technical University, 104 (4), 131–142. https://doi.org/10.33108/visnyk_tntu2021. 04.131.
9. Naik N. (2017). Choice of effective messaging protocols for IoT systems: MQTT, CoAP, AMQP and HTTP. In 2017 IEEE International Systems Engineering Symposium (ISSE) (pp. 1–7). IEEE. https://doi.org/10.1109/syseng.2017.8088251.
10. Al-Masri, E., et al. (2020). Investigating messaging protocols for the Internet of Things (IoT). IEEE Access, 8, 94880-94911. https://doi.org/10.1109/ACCESS.2020.2993363.
11. Thangavel D., Ma X., Valera A., Tan H.-X., & Tan C. K.-Y. (2014). Performance evaluation of MQTT and CoAP via a common middleware. In 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE. https://doi.org/10.1109/issnip.2014. 6827678.
12. Rescorla E. (2018). The Transport Layer Security (TLS) Protocol Version 1.3. RFC 8446. Internet Engineering Task Force. https://doi.org/10.17487/RFC8446.
13. Kumar S., & Patel D. R. (2019) A survey on Internet of Things: Security and privacy issues. International Journal of Computer Applications, 90 (11), 20–26. https://doi.org/10.5120/15764-4454.
14. Hardt D. (2020). The OAuth 2.0 Authorization Framework. RFC 6749 (Updated). Internet Engineering Task Force. https://doi.org/10.17487/RFC6749.
15. Jones M., et al. (2019). OpenID Connect Core 1.0 incorporating errata set 1. OpenID Foundation. https://openid.net/specs/openid-connect-core-1_0.html.
16. Martsenyuk V., & Kit N. (2024) A multivariate method of forecasting the nonlinear dynamics of production network based on multilayer neural models. Scientific Journal of the Ternopil National Technical University, 114 (2), 39–50. https://doi.org/10.33108/visnyk_tntu2024.02.039.
17. Combs G., et al. (2023). Wireshark User's Guide for Wireshark 4.0. Wireshark Foundation. https://www. wireshark.org/docs/wsug_html/.
18. Raschka S. (2018). Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning (Version 3). arXiv. https://doi.org/10.48550/ARXIV.1811.12808.
19. International Electrotechnical Commission. (2018). IEC 61850-5-2018: Communication requirements for functions and device models. IEC Publications.
20. IEEE Standards Association. (2018). IEEE Standard 1613-2018: Environmental and Testing Requirements for Communications Networking Devices in Electric Power Substations. IEEE.
21. A. Voloshchuk, D. Velychko, H. Osukhivska, A. Palamar, (2024). Computer system for energy distribution in conditions of electricity shortage using artificial intelligence, in: Proceedings of the 2nd International Workshop on Computer Information Technologies in Industry 4.0 (CITI 2024), Volume 3742, Ternopil, Ukraine, June 12–14, pp. 66–75. Available at: https://ceur-ws.org/Vol-3742/paper5.pdf.