Interpretable Machine Learning Models for Estimating Electric Energy Consumption in Steel Industries

Paulino José García-Nieto (), Esperanza García-Gonzalo, Luis Alfonso Menéndez-García, Laura Álvarez- de-Prado, Marta Menéndez-Fernández and Antonio Bernardo-Sánchez
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Paulino José García-Nieto: Department of Mathematics, Faculty of Sciences, University of Oviedo, C/Leopoldo Calvo Sotelo, 18, 33007 Oviedo, Spain
Esperanza García-Gonzalo: Department of Mathematics, Faculty of Sciences, University of Oviedo, C/Leopoldo Calvo Sotelo, 18, 33007 Oviedo, Spain
Luis Alfonso Menéndez-García: Department of Mathematics, Faculty of Sciences, University of Oviedo, C/Leopoldo Calvo Sotelo, 18, 33007 Oviedo, Spain
Laura Álvarez- de-Prado: Department of Mining Technology, Topography and Structures, Higher and Technical School of Mining Engineering, University of León, Campus de Vegazana s/n, 24071 León, Spain
Marta Menéndez-Fernández: Department of Mining Technology, Topography and Structures, Higher and Technical School of Mining Engineering, University of León, Campus de Vegazana s/n, 24071 León, Spain
Antonio Bernardo-Sánchez: Department of Mining Technology, Topography and Structures, Higher and Technical School of Mining Engineering, University of León, Campus de Vegazana s/n, 24071 León, Spain

Mathematics, 2025, vol. 13, issue 21, 1-22

Abstract: The substantial energy consumption and associated CO 2 emissions from industrial operations pose significant environmental and economic challenges for factories and surrounding communities. Within the context of industrial energy management, the steel industry represents a major energy consumer. The imperative to optimize energy use in this sector is driven by a combination of environmental concerns, economic incentives, and technological advancements. This study presents a machine learning model that integrates the whale optimization algorithm (WOA) with multivariate adaptive regression splines (MARS) to forecast electric energy consumption. Utilizing a dataset comprising 35,040 real-world energy consumption records from Gwangyang Steelworks in South Korea, the model was benchmarked against other regression techniques (ridge, lasso, and elastic-net), demonstrating that the proposed WOA-MARS approach achieves a significant improvement in the RMSE (vs. elastic-net or lasso regression techniques) while maintaining interpretability through hinge function analysis. The WOA-tuned MARS model achieves a coefficient of determination (R 2 ) of 0.9972, underscoring its effectiveness for energy optimization in steel manufacturing. The key findings reveal that CO 2 emissions and reactive power variables are the strongest predictors.

Keywords: multivariate adaptive regression splines (MARS); whale optimization algorithm (WOA); ridge (RR), lasso (RL), and elastic-net (ENR) regressions; steelworks electric energy consumption (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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