Daily Load Forecasting and Data-Driven Strategies for Steel Industry Based on Random Forest Modeling

• Published in: Applied mathematics and nonlinear sciences Vol. 9; no. 1
• Main Authors: Wang, Siteng, Zhang, Luxi, Cao, Zhiyuan, Zhang, Rui, Zhang, Liwei
• Format: Journal Article
• Language: English
• Published: Sciendo 01.01.2024
• Subjects: 94A16; Daily load forecasting; Gray correlation projection method; Random forest; Situational awareness; Steel industry
• ISSN: 2444-8656; 2444-8656
• DOI: 10.2478/amns-2024-3147

As a large power consumer, the iron and steel industry urgently needs to improve productivity, reduce energy consumption, and save costs by revolutionizing energy management. In this study, we design a power demand management system for the iron and steel industry, and around the load management module, we propose a data-driven strategy based on daily load situational awareness and introduce the random forest model into daily load forecasting in the iron and steel industry. At the same time, the projection principle is applied to improve the traditional gray correlation similar day selection algorithm, and a combination method of daily load forecasting based on the gray projection improved random forest algorithm is proposed. The electric load data of the iron and steel industry in a specific region is utilized as an experimental sample to investigate the model’s forecasting performance and the impact of the data-driven strategy. The model for daily load forecasting in this paper has an average relative error of 1.18%, which is better than other models. The application of the data-driven strategy brought about 6.99% and 6.69% reductions in load demand and basic electricity cost. The data-driven strategy for the steel industry based on the Random Forest model can predict electric loads more accurately and reduce energy costs, as shown by the results.