Methods for Selecting Data Transmission Network Protocols in Energy Systems
DOI:
https://doi.org/10.31861/sisiot2026.1.01002Keywords:
communication protocols, data transmission, energy systems, methods, data analysisAbstract
This paper presents a comprehensive study aimed at enhancing data transmission stability and efficiency in modern energy systems, specifically Smart Grids. The research addresses the critical challenge of optimizing communication protocols within the context of evolving infrastructure and the increasing integration of Distributed Energy Resources. The primary focus of this work is the development of an intelligent communication control system featuring a decision-making mechanism that, based on the analysis of a set of input parameters including network latency, packet loss, and throughput, determines the feasibility of switching between data transmission protocols such as MQTT, CoAP, and HTTPS in real-time. The study employs an ensemble of machine learning classifiers to analyze real-time network telemetry to develop a mechanism for adaptive protocol switching. The proposed methodology integrates the analysis of network traffic dynamics with real-time performance metrics to facilitate dynamic protocol selection. An approach is proposed wherein an ensemble of machine learning models processes a vector of input metrics and classifies the current network state, initiating a protocol switch only when critical threshold values are reached. Experimental validation demonstrates that the adaptive protocol switching system provides improved network traffic scheduling efficiency compared to static implementations. The results indicate significant potential for reducing communication overhead while maintaining high standards of reliability and security in energy system operations. Specifically, the approach prevents data transmission over degraded channels by automatically switching to lightweight protocols like CoAP when connection parameters deteriorate, thereby ensuring compliance with timing requirements. The developed methodology establishes a foundation for the implementation of intelligent communication systems in Smart Grids, contributing to enhanced energy efficiency and grid stability. The findings hold practical value for energy utilities seeking to modernize their infrastructure and optimize data transmission within increasingly complex energy networks.
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