Forecasting of power consumption at mining enterprises using statistical methods
S.M. Karpenko1, N.V. Karpenko2, G.Y. Bezginov1
1 National University of Science & Technology (MISIS), Moscow, Russian Federation
2 Institute of Economics and Finance of the Russian University of Transport, Moscow, Russian Federation
Russian Mining Industry №1 / 2022 р. 82-88
Abstract: Forecasting of electric power consumption with due account of assessed impact of various factors helps to make efficient technical and managerial decisions to optimize the electric power consumption processes, including preparation of bids for the wholesale electric power and capacity market. The article uses multivariate methods of statistical analysis and econometric methods based on time series analysis for model designing. The paper presents the results of developing the following models: a multifactor model of electrical power consumption using the regression analysis, the Principal Component Method with the assessment of the impact of production factors on electrical power consumption using elasticity coefficients, as well as the energy saving factor based on a variable structure model; trend additive and multiplicative forecast models of electrical consumption that take into account the seasonality factor, models with a change in trends, a linear dynamic model of electrical power consumption that takes into account the production output; a forecast adaptive polynomial model of electrical power consumption as well as the Winters model. The developed forecast models have a sufficiently high accuracy (accuracy of the MAPE was below 7%). The choice of the model type to forecast the electrical power consumption depends on the quantitative and qualitative characteristics of the time series, the structural relation between the series, the purpose and objectives of the modeling. In order to enhance the accuracy of the forecast it is required to regularly refine the model and adjust it to the actual situation with the due account of new factors and production trends while building different versions of scenarios and combined forecast models of electrical power consumption.
Keywords: electrical power consumption, forecasting, mining operations, statistical methods
For citation: Karpenko S.M., Karpenko N.V., Bezginov G.Y. Forecasting of power consumption at mining enterprises using statistical methods. Russian Mining Industry. 2022;(1):82–88. https://doi.org/10.30686/1609-9192-2022-1-82-88
Article info
Received: 29.12.2021
Revised: 16.01.2022
Accepted: 21.01.2022
Information about the authors
Sergey M. Karpenko – Cand. Sci. (Eng.), Associate Professor, Department of Energy and Energy Efficiency of the Mining Institute, National University of Science & Technology (MISIS), Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Nadezhda V. Karpenko – Cand. Sci. (Eng.), Associate Professor, Department of Digital Economy Information Systems of the Institute of Economics and Finance, Russian University of Transport, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Gleb Y. Bezginov – Post-Graduate Student, Department of Energy and Energy Efficiency of the Mining Institute, National University of Science & Technology (MISIS), Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
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