Models for operational forecasting of energy consumption in electric arc furnaces using machine learning methods

Authors

  • Saidmurodov Begmurod Rakhimbekovich Ural Federal University named after the first President of Russia B. N. Yeltsin
  • Kokin Sergey Evgenievich Ural Federal University named after the First President of Russia B. N. Yeltsin https://orcid.org/0000-0001-7493-172X

DOI:

https://doi.org/10.25206/1813-8225-2025-195-79-84

Keywords:

energy consumption forecasting, electric arc furnaces, machine learning, neural networks, energy consumption management, parameter optimization, intelligent control systems, Big Data.

Abstract

The article examines models for forecasting the energy consumption of electric arc furnaces using machine learning methods. Classical approaches such as time series analysis, regression models, and exponential smoothing methods are studied, along with modern techniques including gradient boosting (XGBoost, LightGBM) and neural networks (LSTM, CNN). Special attention is given to parameter optimization methods, such as grid search, genetic algorithms, and Bayesian optimization, which enhance the accuracy and adaptability of the models. The advantages of hybrid models integrating classical and machine learning methods to account for linear and nonlinear dependencies are highlighted. Practical applications of the proposed approaches in energy consumption management are discussed, aiming at cost reduction, improved sustainability, and production process optimization.

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Author Biographies

Saidmurodov Begmurod Rakhimbekovich, Ural Federal University named after the first President of Russia B. N. Yeltsin

Postgraduate of the Automated Electrical Systems Department, Ural Federal University named after the First President of Russia B. N. Yeltsin (UrFU), Yekaterinburg.

Kokin Sergey Evgenievich, Ural Federal University named after the First President of Russia B. N. Yeltsin

Doctor of Technical Sciences, Professor, Head of the Automated Electrical Systems Department, UrFU, Yekaterinburg.

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Published

2025-09-22

How to Cite

[1]
Saidmurodov Б.Р. and Kokin С.Е. 2025. Models for operational forecasting of energy consumption in electric arc furnaces using machine learning methods. Omsk Scientific Bulletin. 3(195) (Sep. 2025), 79–84. DOI:https://doi.org/10.25206/1813-8225-2025-195-79-84.

Issue

Section

Energy and Electrical Engineering