Models of Operational Forecasting of Arc Furnace Energy Consumption Using Machine Learning Methods

This paper is an overview of the current research in the field of predicting the energy consumption of electric arc furnaces with emphasis on literature review. Electric arc furnaces play a key role in the metallurgical and metalworking industries, and optimizing their energy consumption is a crucial task to improve production efficiency. This paper analyzes the state-of-the-art machine learning methods used to predict the energy consumption of electric arc furnaces. Special attention is given to the literature review, which is a comparative analysis of previous research in this area. Various methods including neural networks, regression analysis, time series and others are reviewed. The literature review not only provides an assessment of the current state of research, but also identifies limitations and potential directions for future research. The conclusions and recommendations presented in the paper can serve as a basis for further research in the field of optimizing energy consumption in electric arc furnaces and improving the efficiency of metallurgical production.