M.S. Antonenko1, M.S. Anisimova2, M.V. Kolistratov3

1–3 National Research Technological University «MISIS» (Moscow, Russia)
1 antonenko.ms@misis.ru, 2 anisimova.ms@misis.ru, 3 kolistratov.mv@misis.ru

Problem statement. Large industrial enterprises face high energy costs, which, in the context of rising prices and the introduction of energy-intensive technologies, creates financial risks and requires a systematic approach to energy consumption management. Existing automated accounting systems (ASTUE, AIS TUE) often do not provide the necessary depth of analysis and adaptability for effective energy management, especially in enterprises with a heterogeneous cost structure. The purpose of the research is to develop approaches to the creation and implementation of digital energy management systems in large industrial enterprises, including analytical modules based on mathematical modeling and machine learning methods. Results. The paper substantiates the need to modernize traditional accounting systems towards intelligent platforms with an object-oriented architecture. The architecture of the analytical module is proposed, which includes mathematical models (regression analysis, least squares method) and methods for predicting the remaining life of equipment (Kaplan–Mayer method). Using the example of three-phase ore-thermal and arc furnaces, the possibility of increasing energy efficiency is shown by improving ACS control algorithms, temperature control and electrode position, as well as predictive diagnostics of lining condition. Practical significance. The implementation of the proposed solutions allows industrial enterprises to reduce the specific consumption of electricity, optimize the cost of purchasing energy resources through precise planning, reduce the number of unplanned downtime and extend the service life of equipment.

Antonenko M.S., Anisimova M.S., Kolistratov M.V. Integrated energy management systems in industry: methods, models and practical implementation using the example of arc furnaces. Dynamics of complex systems. 2026. V. 20. № 3. P. 74−81. DOI: 10.18127/j19997493-202603-08 (in Russian).

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