Load forecasting for iron and steel industry based on hybrid mechanism- and data-driven

Yi Zhang, Shouquan Sun, Chuandong Li, Jieyu Ou and Jintao Chen

Energy, 2025, vol. 328, issue C

Abstract: —The electrical load of the iron and steel industry is significantly influenced by physical parameters and production flow. However, the current research neglects the interconnected nature of consecutive processes, resulting in imprecise simulations. To address these issues, a hybrid mechanism- and data-driven load forecasting model for the iron and steel industry is proposed. Firstly, the attenuation law of physical variables between successive processes is established based on the coupling characteristics of material and energy flow during transportation. Then, a general expression of power function is developed considering the equipment's operational characteristic. Additionally, physical quantities and characteristic parameters of the power function are fitted using Kernel-Based Extreme Learning Machine (KELM). Finally, the predictive power of different processes is combined in time domain to derive the total power curve of the iron and steel industry. Through case studies on the iron and steel industry, the accuracy and superiority of the model proposed in this paper have been verified. The error of the hybrid-driven model is reduced by 24.01 % and 16.55 % respectively compared with the mechanism-driven model and the data-driven model, highlighting its improved accuracy in capturing the enterprise's load dynamics.

Keywords: Steel enterprises; Mechanism- and data-driven; Material flow; Energy flow; Power characteristics (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:328:y:2025:i:c:s0360544225022340

DOI: 10.1016/j.energy.2025.136592

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