Towards Better Energy Efficiency Through Coil-Based Electricity Consumption Forecasting in Steel Manufacturing

Abstract

Forecasting electricity consumption with the possibly-highest accuracy is crucial for cost optimization, operational efficiency, competitiveness, contract negotiation, and achieving the global goals of sustainable development in steel manufacturing. This study focuses on identifying the most appropriate prediction algorithm for coil-based electricity consumption and the most effective implementation purposes in a steel company. Random Forest, Gradient-Boosted Trees, and Deep Neural Networks are preferred because they are suitable for the given problem and widely used for forecasting. The performance of the prediction models is evaluated based on the root mean squared error (RMSE) and the coefficient of determination (R-squared). Experiments show that the Random Forest model outperforms the Gradient-Boosted Trees and Deep Neural Network models. The results will provide benefits for many different purposes. Firstly, during contract negotiations, it will enable us to gain a competitive advantage when purchasing electricity in the day-ahead market. Secondly, in the production scheduling phase, the ones with the highest electricity consumption will be produced during the hours when there is the least demand at the most affordable prices. Finally, when prioritizing sales orders, the use of the existing capacity for orders with lower energy intensity or a higher profit margin will be ensured.