Models for operational forecasting of energy consumption in electric arc furnaces using machine learning methods
https://doi.org/10.25206/1813-8225-2025-195-79-84
EDN: JDQQCC
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.
About the Authors
B. R. Saidmurodov
Ural Federal University named after the First President of Russia B. N. Yeltsin
Russian Federation
Russian Federation
SAIDMURODOV Begmurod Rakhimbekovich, Postgraduate of the Automated Electrical Systems Department
Yekaterinburg
S. E. Kokin
Ural Federal University named after the First President of Russia B. N. Yeltsin
Russian Federation
Russian Federation
KOKIN Sergey Evgenievich, Doctor of Technical Sciences, Professor, Head of the Automated Electrical Systems Department
AuthorID (RSCI): 610636
AuthorID (SCOPUS): 24577707300
Yekaterinburg
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Review
For citations:
Saidmurodov BR, Kokin SE. Models for operational forecasting of energy consumption in electric arc furnaces using machine learning methods. Omsk Scientific Bulletin. 2025;(3):79-84. (In Russ.) https://doi.org/10.25206/1813-8225-2025-195-79-84. EDN: JDQQCC
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