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

Abstract

The substantial energy consumption and associated CO2 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 (R2) of 0.9972, underscoring its effectiveness for energy optimization in steel manufacturing. The key findings reveal that CO2 emissions and reactive power variables are the strongest predictors.