Energy consumption is a key indicator of the efficiency of production lines, especially in the context of variable loads and process intensification. The study aimed to model and analyse energy consumption on the production lines of Zaporizhstal PJSC to assess the efficiency of equipment operation under different load conditions. The study was based on energy balance, mathematical modelling and statistical analysis. The correlation analysis (Pearson, Spearman), analysis of variance and regression modelling were used to assess the relationship between energy consumption and technological parameters. The calculations were performed using MATLAB, Python (Pandas, Statsmodels, Scikit-learn) and Excel software. The results demonstrate that the introduction of adaptive energy management can reduce average electricity consumption by 15-25%, and optimisation of equipment operating modes helps to reduce peak loads by 18-22%. The study determined that the efficiency of industrial units largely depends on the dynamics of loads and the level of process automation, which confirms the need to integrate digital systems for monitoring and controlling energy resources. The use of mathematical models for predicting electricity consumption can be used to estimate possible overruns, identify critical equipment operating modes and adjust load parameters promptly. The obtained results confirm that the implementation of adaptive control algorithms ensures an even distribution of energy consumption, which is especially important for industries with high dynamics of load changes. The proposed models can be used to improve the efficiency of industrial enterprises, reduce energy costs and optimise the management of production processes
Lyudmyla Mikhailova (Sun,) studied this question.