Seasonal and long-term trends in power consumption of technological units of the mining and processing plant

The article is devoted to the urgent problem of increasing the energy efficiency of mining enterprises, which directly affects the cost of production and the competitiveness of the industry. Despite the steady growth of electricity consumption, many enterprises do not use a systematic approach to rationing, accounting and forecasting energy costs. Object of study: the power supply system of the Madneulsky Mining and Processing Plant. The purpose of the work is to conduct a comprehensive analysis of the enterprise’s energy consumption, identify patterns and dependencies, develop regression models for forecasting electricity and propose measures to optimize equipment operating modes. The methods of statistical analysis, regression modeling (linear, exponential and parabolic approximation), Fisher’s criterion for checking the adequacy of models, calculation of confidence intervals, as well as analysis of time series of electricity consumption over a 5-year period were used. Regression models of power consumption for each division were built, seasonal dependencies were identified, average quarterly and average annual indicators were calculated, confidence intervals were determined. It was found that the most significant seasonal fluctuations were observed in the overburden, barite ore loading, basalt stone mining and crushing divisions. It was proposed to use linear regression models for engineering calculations as the simplest and most adequate. Divisions with unstable and stable power consumption were identified, which allows differentiating the approach to energy saving. The results of the study can be used to develop energy saving programs, plan electricity purchases, optimize tariff decisions and implement automated energy resource monitoring and accounting systems.

Klyuev R. V. Seasonal and long-term trends in power consumption of technological units of the mining and processing plant. MIAB. Mining Inf. Anal. Bull. 2025;(11):153-165. DOI: 10.25018/0236_1493_2025_11_0_153.

R.V. Klyuev, Dr. Sci. (Eng.), Assistant Professor, Professor, Moscow Polytechnic University, 107023, Moscow, Russia, e-mail: kluev-roman@rambler.ru, ORCID ID: 0000-0003-3777-7203.

1. Petrov V. L., Kuznetsov N. M., Morozov I. N. Modeling power use at processing plant. Gornyi Zhurnal. 2022, no. 2, pp. 72—76. [In Russ]. DOI: 10.17580/gzh.2022.02.11.

2. Zhukovskiy Yu.L., Suslikov P. K. Assessment of the potential effect of applying demand management technology at mining enterprises. Sustainable Development of Mountain Territories. 2024, vol. 16, no. 3, pp. 895—908. [In Russ]. DOI: 10.21177/1998-4502-2024-16-3-895-908.

3. Kapansky A. A. Methods for solving the problems of evaluation and forecasting energy efficiency. Kazan state power engineering university bulletin. 2019, vol. 11, no. 2 (42), pp. 103—115. [In Russ].

4. Wang Y., Zhang N., Chen X. A short-term residential load forecasting model based on lstm recurrent neural network considering weather features. Energies. 2021, vol. 14, article 2737. [In Russ]. DOI: 10.3390/en14102737.

5. Zhukovskiy Y., Batueva D., Buldysko A., Shabalov M. Motivation towards energy saving by means of IoT personal energy manager platform. Journal of Physics: Conference Series. 2019, vol. 1333, no. 6, article 062033. [In Russ]. DOI: 10.1088/1742-6596/1333/6/062033.

6. Sokolov A. A., Fomenko O. A., Ignatev I. V. Development of algorithms for control and control of electric power parameters based on information-measuring system data. Journal of Physics: Conference Series. 2022, vol. 2176, no. 1, article 012076. [In Russ]. DOI: 10.1088/1742-6596/2176/1/012076.

7. Zinovieva O. M., Merkulova A. M., Smirnova N. A., Chadin D. A. Assessment of the reliability of technological processes at enrichment plants taking into account their automation. Sustainable Development of Mountain Territories. 2025, vol. 17, no. 1, pp. 437—447. [In Russ]. DOI: 10.21177/19984502-2025-17-1-437-447.

8. Shchemeleva Y. B., Sokolov A. A., Lobazanova S. H. Development of hardware and a system for analyzing energy parameters based on simulation in SimInTech. Journal of Physics: Conference Series. 2022, vol. 2176, no. 1, article 012082. [In Russ]. DOI: 10.1088/1742-6596/2176/1/012082.

9. Morgoev I., Klyuev R., Morgoeva A. Methodology for detecting non-technical energy losses using an ensemble of machine learning algorithms. CMES Computer Modeling in Engineering and Sciences. 2025, vol. 143, no. 2, pp. 1381—1399. [In Russ]. DOI: 10.32604/cmes.2025.064502.

10. Shishkin P. V., Malozyomov B. V., Martyushev N. V., Sorokova S. N., Efremenkov E. A., Valuev D. V., Qi M. Mathematical logic model for analysing the controllability of mining equipment. Mathematics. 2024, vol. 12, no. 11, article 1660. [In Russ]. DOI: 10.3390/math12111660.

11. Malozyomov B. V., Martyushev N. V., Sorokova S. N., Efremenkov E. A., Valuev D. V., Qi M. Analysis of a predictive mathematical model of weather changes based on neural networks. Mathematics. 2024, vol. 12, no. 3, article 480. [In Russ]. DOI: 10.3390/math12030480.

12. Klyuev R. V. Reliability analysis of open-pit power supply system components. Mining Science and Technology (Russia). 2024, vol. 9, no.2, pp. 183—194. [In Russ]. DOI: 10.17073/2500-0632-2024-03-254.

13. Balovtsev S. V., Merkulova A. M. Comprehensive assessment of buildings, structures and technical devices reliability of mining enterprises. MIAB. Mining Inf. Anal. Bull. 2024, no. 3, pp. 170—181. [In Russ]. DOI: 10.25018/0236_1493_2024_3_0_170.

14. Klyuev R. V. Analysis of technological and energy parameters of ball mills. Russian Mining Industry Journal. 2024, no. 6, pp. 107—110. [In Russ]. DOI: 10.30686/1609-9192-2024-6-107-110.

15. Oprea S. V., Bara A., Ifrim G. Flattening the electricity consumption peak and reducing the electricity payment for residential consumers in the context of smart grid by means of shifting optimization algorithm. Computers & Industrial Engineering. 2018, vol. 122, pp. 125—139. [In Russ]. DOI: 10.1016/j.cie.2018.05.053.

16. Patil P. D., Patil R., Ahire P., Bharati R., Dongre Y. An adaptive methodology based on predictive deep learning and context aware clustering for electricity power usage mining and optimization at different granularity levels. e-Prime — Advances in Electrical Engineering, Electronics and Energy. 2024, vol. 8, article 100628. [In Russ]. DOI: 10.1016/j.prime.2024.100628.

17. Vyalkova S. A., Morgoeva A. D., Gavrina O. A. Development of a hybrid model for predicting the consumption of electrical energy for a mining and metallurgical enterprise. Sustainable Development of Mountain Territories. 2022, vol. 14, no. 3, pp. 486—493. [In Russ]. DOI: 10.21177/1998-45022022-14-3-486-493.

18. Albuquerque P. C., Cajueiro D. O., Rossi M. D. C. Machine learning models for forecasting power electricity consumption using a high dimensional dataset. Expert Systems with Applications. 2022, vol. 187, article 115917. [In Russ]. DOI: 10.1016/J.ESWA.2021.115917.

19. Nazarychev A. N., Dyachenok G. V., Sychev YU.A. A reliability study of the traction drive system in haul trucks based on failure analysis of their functional parts. Journal of Mining Institute. 2023, vol. 261, pp. 363—373. [In Russ].

20. Ji Q., Zhang S., Duan Q., Gong Y., Li Y., Xie X., Bai J., Huang C., Zhao X. Shortand mediumterm power demand forecasting with multiple factors based on multi-model fusion. Mathematics. 2022, vol. 10, article 2148. [In Russ]. DOI: 10.3390/math10122148.