Balancing the production and consumption of electricity is an urgent task. Its implementation largely depends on the means and methods of planning electricity production. Forecasting is one of the planning tools since the availability of an accurate forecast is a mechanism for increasing the validity of management decisions. This study provides an overview of the methods used to predict electricity supply requirements to different objects. The methods have been reviewed analytically, taking into account the forecast classification according to the anticipation period. In this way, the methods used in operative, short-term, medium-term, and long-term forecasting have been considered. Both classical and modern forecasting methods have been identified when forecasting electric energy consumption. Classical forecasting methods are based on the theory of regression and statistical analysis (regression, autoregressive models); probabilistic forecasting methods and modern forecasting methods use classical and deep-machine-learning algorithms, rank analysis methodology, fuzzy set theory, singular spectral analysis, wavelet transformations, Gray models, etc. Due to the need to take into account the specifics of each subject area characterizing an energy facility to obtain reliable forecast results, power consumption modeling remains an urgent task despite a wide variety of other methods. The review was conducted with an assessment of the methods according to the following criteria: labor intensity, requirements for the initial data set, scope of application, accuracy of the forecasting method, the possibility of application for other forecasting horizons. The above classification of methods according to the anticipation period allows highlights the fact that when predicting power consumption for different time intervals, the same methods are often used. Therefore, it is worth emphasizing the importance of classifying the forecast over the forecasting horizon not to differentiate the methods used to predict electricity consumption for each period but to consider the specifics of each type of forecasting (operative, short-term, medium-term, long-term).

In this paper, a new solution is given to an actual scientific problem of increasing the efficiency of power supply systems using renewable energy sources, aimed at optimizing power supply systems using solar power stations for consumers. The article calculates and selects the solar power supply for the autonomous power supply of the recreation center in the highlands. An analysis of the overall situation of solar energy in the world was presented. It is revealed that due to the length of the territory of Russia, the levels of solar radiation in different regions vary considerably. The useful specific solar energy of the Tsey gorge in the Alagir region of the Republic of North Ossetia-Alania has been determined. Various factors contributing to an efficiency increase of a solar power plant are considered. The use of a solar power station for electricity supply to consumers in mountainous areas can be considered economically justified, assuming that the payback period was 6.7 years, while the average payback period in the Russian Federation for stations of this type is 17 years.

Development of new deposits and modernization of existing mining enterprises in order to improve their efficiency are the priorities for the development of tungsten-molybdenum resource sector. In the modernization of mining productions, attention is paid to all technological processes and engineering systems, transformation of which should be aimed at improving economic performance, including reliability. Research of power supply systems of mining enterprises and modes of their operation is an urgent task, as this type of engineering support of mining works, auxiliary processes and facilities determines competitiveness of an enterprise as a whole. In the course of research, an assessment of operational reliability of power supply of developing ore mining areas at a high-altitude mine of the Tyrnyauz deposit (Kabardino-Balkaria) was performed. It was found that under the existing scheme of separate power supply of 6 kV networks from substations, the full ground fault currents for these networks are respectively: I₀₃₀=17.5 A and I₀₁₁₆ =12.2 A and reach the limiting values at which it was possible to disconnect them by disconnectors (the limiting current was 20 A). A formula for total ground fault current in 6 kV underground networks was recommended. It was shown that reliability of power supply in the above mine, as well as the level of danger of electric shock in electrical installations depended largely on the number of singlephase ground faults. On the basis of the theoretical and experimental research, the solutions to improve the operation of electrical networks, taking into account the structure and modes of operation of a highaltitude mine were developed.

The relevance of the research is caused by the need to develop energy-saving approaches through the use of data mining tools to improve the efficiency of the management decision-making process and, as a result, more optimal use of energy resources. In particular, forecasting the consumption of electric energy of an industrial facility will simplify the process of making managerial decisions and will help minimize the cost of electricity for the production of a unit of production. The availability of an accurate forecast will enable the use of reserve capacities during peak load hours for the electric power complex. In practice, the existing methods of calculating the load on the power grid are not always suitable for forecasting, so the study is interdisciplinary in nature, combining important practical significance and the development of new recommendations regarding the use of machine learning algorithms. The main aim of the research is to analyze scientific papers containing proposals to improve the accuracy of determining energy loads using data mining, as well as to develop a machine learning model that allows you to create a reliable forecast of electricity consumption for an industrial enterprise. Objects of the research is an industrial enterprise that is characterized by the complexity of determining the energy characteristics of technological equipment. Methods: analytical method, methods of mathematical statistics, methods of machine learning, complex generalization of scientific achievements and practical experience in the use of data processing tools in the tasks of predicting the load on the power grid. Results. A review of literature sources covering the application of data mining in energy consumption management is carried out, and the main results of forecasting total electricity consumption according to industrial facility data are presented. The methods of data mining used to solve energy saving problems for various objects are considered. A machine learning model is built based on the gradient boosting algorithm of the CatBoost library, which allows obtaining a forecast of electricity consumption by months with a reliability level of 92 %. The results of the study are relevant for decision-making at the tactical and strategic levels of enterprise management for medium-term (monthly) and long-term (from a year to several years) forecasting of electrical loads, respectively. Актуальность исследования заключается в необходимости развития энергосберегающих подходов за счет применения средств интеллектуального анализа данных для повышения эффективности процесса принятия управленческих решений и, как следствие, более оптимального использования энергетических ресурсов. В частности, прогнозирование потребления электрической энергии промышленного объекта упростит процесс принятия управленческих решений и будет способствовать минимизации затрат электроэнергии на производство единицы продукции. Наличие точного прогноза позволит задействовать резервные мощности в часы пиковой нагрузки на электроэнергетический комплекс. На практике для реализации прогнозирования не всегда подходят существующие методы расчета нагрузки на электросеть, поэтому исследование носит междисциплинарный характер, сочетая в себе важную практическую значимость и выработку новых рекомендаций в части применения алгоритмов машинного обучения. Целью исследования является анализ научных работ, содержащих предложения по повышению точности определения энергетических нагрузок с помощью интеллектуального анализа данных, а также разработка модели машинного обучения, позволяющей создавать достоверный прогноз потребления электроэнергии для промышленного предприятия. Объекты: промышленное предприятие, которое характеризуется сложностью определения энергетических характеристик технологического оборудования. Методы: аналитический метод, методы математической статистики, методы машинного обучения, комплексного обобщения научных достижений и практического опыта применения средств обработки данных в задачах прогнозирования нагрузки на электросеть. Результаты. Проведен обзор литературных источников, освещающих вопросы применения интеллектуального анализа данных в управлении энергопотреблением, и представлены основные результаты прогнозирования суммарных объемов потребления...