Authors: I.V. Chicherin, V.V. Slesarenko

Title of the article: Electrotechnical processes of energy consumption management in smart grid

Year: 2026, Issue: 2, Pages: 32-38

Branch of knowledge: 2.4.2. Electrotechnical complexes and systems (engineering)

Index UDK: 621.31

DOI: 10.26730/1816-4528-2026-2-32-38

Abstract: The relevance of the study is driven by the need to enhance the efficiency and reliability of power systems amid the integration of distributed energy resources and renewable generation. Intelligent energy consumption control is a key aspect of smart grid development, enabling real-time balancing between generation, consumption, and energy storage. The article presents the development and analysis of a comprehensive methodology for energy management based on machine learning, SCADA systems, and energy storage technologies. The study focuses on real-time load balancing, energy consumption forecasting, and optimization of generator and energy storage operation. To achieve these goals, modeling and experimental testing were conducted using a laboratory setup equipped with IoT sensors and a 10 kW / 40 kWh lithium-ion battery system. Load forecasting was implemented using LSTM recurrent neural networks, while energy consumption optimization was performed using linear programming methods. The results demonstrate improved forecasting accuracy and reduced peak loads, contributing to more stable and energy-efficient power system operation. The proposed approach proves to be effective and applicable to the design of intelligent control systems in modern electric power networks.

Key words: smart grids energy management load forecasting SCADA systems LSTM models linear programming energy storage systems

Receiving date: 16.09.2025

Approval date: 15.03.2026

Publication date: 04.06.2026

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