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).

Relevance. The need to develop energy-saving approaches through the use of data mining tools to improve the efficiency of management decision-making and more optimal use of energy resources. Forecasting the amount of electric energy generated by a solar power plant will allow optimal electricity distribution in decentralized systems. Information about the amount of electricity generated by a solar power plant and transmitted to a grid at every hour will allow planning the use of generated electricity and its distribution more reasonably. Also, the presence of a reliable forecast will allow embedding a predictive model into the micro-grid management subsystem. This will facilitate the integration of centralized electrical networks and distributed generation facilities. Aim. To analyze scientific papers containing proposals to improve the accuracy of determining the amount of electricity generated by solar power plants; to create machine learning models that allow you to create a short-term forecast of electricity generation by a solar power plant. Objects. Solar power plant named after A.A. Vlaznev (Sakmarskaya SPP), Orenburg region, Orsk, Oktyabrsky district, geographical coordinates: 51.266023, 58.474689. 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 forecasting electricity generation by solar power plants. Results. The authors have carried out the review of literature sources reflecting the results of using modern data mining tools in predicting the magnitude of electricity generation by solar power plants. The paper considers various approaches to forecasting electricity generation at solar power plants. The analysis of factors used in forecasting is carried out. The authors obtained the results of theoretical and applied nature. The results consist in recommendations on using exogenous variables in predicting power generation at SPP, as well as some machine learning algorithms in construction of predictive models. These recommendations were obtained in generalizing the results of the applied research.

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...

The widespread deployment of advanced metering infrastructure (AMI) and smart grid technologies has significantly expanded the availability of high-resolution electricity consumption data in distribution networks. This creates new opportunities for addressing non-technical losses (NTL). This paper presents a systematic literature review of data-driven methods for NTL detection based on an analysis of 79 relevant scientific publications from leading databases (IEEE Xplore, Springer, MDPI, and ScienceDirect) indexed in Scopus/Scimago over the period 2020–2025. The reviewed methods are categorized according to a novel triad of criteria: the analytical paradigm, the input data structure, and the required level of grid digitalization. A comparative analysis reveals distinct performance profiles: supervised classification methods, particularly hybrid and ensemble models, enable high-accuracy detection, with many studies reporting F1-scores exceeding 0.95 on benchmark datasets, yet they require extensive labeled data from AMI systems. Unsupervised clustering techniques offer a practical alternative for grids with partial or no AMI by analyzing aggregated consumption patterns. Forecasting-based (regression) methods facilitate continuous consumption monitoring and anomaly detection via deviation analysis, while scenario-modeling techniques provide strategic tools for evaluating the potential impact of NTL reduction measures. This review critically examines the causes of NTL, feature engineering strategies, software tools, and key challenges such as data imbalance, model interpretability, and real-world deployment constraints. The synthesis provides a utility-centric framework for method selection, offering practical recommendations tailored to different infrastructure profiles. The proposed systematization bridges methodological gaps and supports the development of context-aware, data-driven solutions for modern and evolving power systems. Received: 3 July 2025 | Revised: 8 January 2026 | Accepted: 14 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Irbek D. Morgoev: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Roman V. Klyuev: Conceptualization, Formal analysis, Writing – review & editing, Supervision. Angelika D. Morgoeva: Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Project administration.

В статье представлен алгоритм поддержки принятия решений, позволяющий оперативно выявлять безучетное потребление электроэнергии у бытовых потребителей. Рассматриваемый подход позволяет отслеживать аномальные значения потребления и принимать своевременные адресные и эффективные управленческие решения. Разработанный алгоритм был экспериментально апробирован в секторе бытовых потребителей на основе имеющихся данных с использованием методов машинного обучения и имитационного моделирования. Результаты эксперимента показали перспективность применения электросетевой компанией предложенного подхода к решению проблемы выявления безучетного потребления электроэнергии. Предлагаемый алгоритм позволил из множества потребителей электроэнергии выявлять подмножество с аномальным электропотреблением, тем самым сузив поле поиска потребителей с безучетным потреблением более чем в 12...