Mathematical model of electricity consumption by enterprises of the chernihiv region
DOI:
https://doi.org/10.31548/energiya1(83).2026.055Keywords:
electricity, regression analysis, mathematical model, forecasting, industry, Chernihiv regionAbstract
The article examines the dynamics of electricity consumption by enterprises in Chernihiv Oblast and presents a forecast of electricity consumption for 2026. The relevance of the study is determined by the need to improve the efficiency of energy resource management in enterprises under conditions of economic instability caused by the full-scale war in Ukraine and the global energy crisis. Therefore, issues of energy consumption planning, ensuring the continuity of production processes, and improving energy efficiency are becoming particularly important for the stable functioning of the regional economy of Chernihiv Oblast.
The aim of this study is to develop a mathematical model and forecast electricity consumption by enterprises in Chernihiv Oblast based on statistical data for the period 2016–2024. Official data from the State Statistics Service of Ukraine on electricity consumption for production, operational, and business needs of enterprises, excluding electricity supplied to households, were used in this study.
Methods of descriptive statistics, correlation analysis, and regression analysis implemented in Microsoft Excel were applied. As a result of the correlation field analysis, several types of regression models were considered. The cubic polynomial function proved to be the most suitable, with a coefficient of determination R² = 0.9303, indicating a high level of agreement between the model and empirical data.
Based on the constructed empirical polynomial model, a point forecast of electricity consumption for 2026 was obtained, amounting to 724,274 thousand kWh. The results of the study can be used for planning and improving the efficiency of energy resource management in Chernihiv Oblast.
Future research prospects include extending the model by incorporating dominant influencing factors and applying machine learning methods.
Recieved 2025-11-27
Recieved 2026-01-30
Accepted 2026-02-11
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