1. Kubrin S. S., Reshetnyak S. N., Zakorshmenny I. M., Karpenko S. M. Simulation modeling of equipment operating modes of complex mechanized coal mine face. Sustainable Development of Mountain Territories. 2022, vol. 14, no. 2, pp. 286—294. [In Russ]. DOI: 10.21177/19984502-2022-14-2-286-294.
2. Balovtsev S. V. Higher rank aerological risks in coal mines. Mining Science and Technology (Russia). 2022, vol. 7, no. 4, pp. 310—319. [In Russ]. DOI: 10.17073/2500-0632-2022-08-18.
3. Petrov V. L., Kuznetsov N. M., Morozov I. N. Electric energy demand management in mining industry using smart power grids. MIAB. Mining Inf. Anal. Bull. 2022, no. 2, pp. 169—180. [In Russ]. DOI: 10.25018/0236_1493_2022_2_0_169.
4. 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: 10.21177/1998-4502-2022-14-3-486-493.
5. Laayati O., Bouzi M., Chebak A. Smart energy management system: design of a monitoring and peak load forecasting system for an experimental open-pit mine. Applied System Innovation. 2022, vol. 5, no. 1, article 18. DOI: 10.3390/asi5010018.
6. Klyuev R. V., Morgoev I. D., Morgoeva A. D., Gavrina O. A., Martyushev N. V., Efremenkov E. A., Mengxu Q. Methods of forecasting electric energy consumption: A literature review. Energies. 2022, vol. 15, no. 23, article 8919. DOI: 10.3390/en15238919.
7. Morgoeva A. D., Morgoev I. D., Klyuev R. V., Gavrina O. A. Forecasting of electric energy consumption by an industrial enterprise using machine learning methods. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering. 2022, vol. 333, no. 7, pp. 115—125. [In Russ]. DOI: 10.18799/24131830/2022/7/3527.
8. Shklyarskiy J. E., Batueva D. E. The influence of external climatic factors on the accuracy of the forecast of energy consumption. E3S Web of Conferences. 2019, vol. 140, article 04014. DOI: 10.1051/e3sconf/201914004014.
9. Rollert K. E. The underlying factors in the uptake of electricity demand response: The case of Poland. Utilities Policy. 2018, vol. 54, pp. 11—21. DOI: 10.1016/j.jup.2018.07.002.
10. Biel K., Glock C. Systematic literature review of decision support models for energy-efficient production planning. Computers & Industrial Engineering. 2016, vol. 101, pp. 243—259. DOI: 10.1016/j.cie.2016.08.021.
11. Lago J., Marcjasz G., De Schutter B., Weron R. Forecasting day-ahead electricity prices. A review of state-of-the-art algorithms, best practices and an open-access benchmark. Applied Energy. 2021, vol. 293, article 116983. DOI: 10.1016/j.apenergy.2021.116983.
12. Kondratiev Yu. I., Sokolova О. А., Kambolov D. A, Miroshnikov A. S. Electrochemical leaching of polymetallic ore under the action of asymmetric current pulses and the addition of a surfactant. Sustainable Development of Mountain Territories. 2022, vol. 14, no. 1, pp. 20—26. [In Russ]. DOI: 10.21177/1998-4502-2022-14-1-20-26.
13. Senchilo N. D., Ustinov D. A. Method for determining the optimal capacity of energy storage systems with a long-term forecast of power consumption. Energies. 2021. vol. 14, no. 21, article 7098. DOI: 10.3390/en14217098.
14. Novikova A. Energy storage: technologies and trends. Rynok elektrotekhniki. 2022, no. 4(68), pp. 6—25. [In Russ].
15. Ibrahim B., Rabelo L. A deep learning approach for peak load forecasting: A case study on Panama. Energies. 2021, vol. 14, no. 11, article 3039. DOI: 10.3390/en14113039.
16. 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, no. 10, article 2737. DOI: 10.3390/en14102737.
17. Dorado R. F., Suarez J. D., del Real Torres A. Short-term load forecasting using encoderdecoder WaveNet: Application to the french grid. Energies. 2021, vol. 14, no. 9, article 2524. DOI: 10.3390/en14092524.
18. Xie Y., Yang Y., Wu L. Power consumption forecast of three major industries in China based on fractional grey model. Axioms. 2022, vol. 11, no. 8, article 407. DOI: 10.3390/axioms11080407.
19. Khan S., Aslam S., Mustafa I., Aslam S. Short-term electricity price forecasting by employing ensemble empirical mode decomposition and extreme learning machine. Forecasting. 2021, vol. 3, no. 3, pp. 460—477. DOI: 10.3390/forecast3030028.
20. Abu-Salih B., Wongthongtham P., Morrison G., Coutinho K., Al-Okaily M., Huneiti A. Short-term renewable energy consumption and generation forecasting. A case study of Western Australia. Heliyon. 2022, vol. 8, no. 3, article e09152. DOI: 10.1016/j.heliyon.2022.e09152.
21. Szul T., Nęcka K., Lis S. Application of the takagi-sugeno fuzzy modeling to forecast energy efficiency in real buildings undergoing thermal improvement. Energies. 2021, vol. 14, no. 7, article 1920. DOI: 10.3390/en14071920.
22. Ramos D., Khorram M., Faria P., Vale Z. Load forecasting in an office building with different data structure and learning parameters. Forecasting. 2021, vol. 3, no. 1, pp. 242—255. DOI: 10.3390/forecast3010015.
23. Oprea S.-V., Pirjan A., Caruțașu G., Petroșanu D.-M., Bara A., Stanica J.-L., Coculescu C. Developing a Mixed neural network approach to forecast the residential electricity consumption based on sensor recorded data. Sensors. 2018, vol. 18, no. 5, article 1443. DOI: 10.3390/s18051443.
24. Frikha M., Taouil K., Fakhfakh A., Derbel F. Limitation of deep-learning algorithm for prediction of power consumption. Engineering Proceedings. 2022, vol. 18, no. 1, article 26. DOI: 10.3390/engproc2022018026.