Digital twin is one of the emerging technologies for the digital transformation of the power industry. Many existing studies claim that the widespread application of digital twins will shift the industry to a principally new level of development. This article provides an extensive overview of the industrial application experience of digital twin technologies for solving the problems of modern power systems with a particular focus on the task of high-voltage power equipment lifecycle management. The latter task contours one of the most promising areas for the application of the digital twins in the power industry since it requires deep analysis of the technological processes dynamics and the development of physical, mathematical and computer models that cover all the potential benefits of the digital twin technology. At the moment, there is a lack of reliable data on the problems of assessing and predicting the technical state of high-voltage power equipment. The use of digital twin technology in modern power systems will allow for aggregating data from a variety of real objects and will allow the automatization of collecting and processing of big data by implementing artificial intelligence methods, which will ultimately make it possible to manage the life cycle of the power equipment. The article puts to scrutiny the industrial experience of digital twins creation, considering the technical solutions suggested by the largest manufacturers of electrical equipment. A classification of digital twins, examples and main features of their application in the power industry, including the problem of managing the life cycle of high-voltage electrical equipment, are considered and discussed.

The rapid increase in solar power plant installed capacity leads to considerable difficulties in terms of power system operation and control, resulting from highly stochastic nature of solar energy harvesting. The paper considers the problem of day-ahead solar power plant output forecasting, based on the meteorological data. The improvement of solar power plant output prediction will significantly simplify power system operation mode planning taking into market procedures and active power generation reserves allocation. As a case study the authors use meteorological data for a real operated solar power plant. As a results of regression modelling the statistical significance of the meteorological parameters was analyzed. The optimal mathematical formulation of regression model was provided. In addition, the paper gives the idea of empirical clasterization approach, providing significant improvement of prediction accuracy. The results of the verification on real data allow deciding on the applicability of the proposed methods in industrial operation.

Currently, machine learning methods are widely applied in the power industry to solve various tasks, including short-term power consumption forecasting. However, the lack of interpretability of machine learning methods can lead to their incorrect use, potentially resulting in electrical system instability or equipment failures. This article addresses the task of short-term power consumption forecasting, one of the tasks of enhancing the energy efficiency of gas industry enterprises. In order to reduce the risks of making incorrect decisions based on the results of short-term power consumption forecasts made by machine learning methods, the SHapley Additive exPlanations method was proposed. Additionally, the application of a multi-agent approach for the decomposition of production processes using self-generation agents, energy storage agents, and consumption agents was demonstrated. It can enable the safe operation of critical infrastructure, for instance, adjusting the operation modes of self-generation units and energy-storage systems, optimizing the power consumption schedule, and reducing electricity and power costs. A comparative analysis of various algorithms for constructing decision tree ensembles was conducted to forecast power consumption by gas industry enterprises with different numbers of categorical features. The experiments demonstrated that using the developed method and production process factors reduced the MAE from 105.00 kWh (MAPE of 16.81%), obtained through expert forecasting, to 15.52 kWh (3.44%). Examples were provided of how the use of SHapley Additive exPlanation can increase the safety of the electrical system management of gas industry enterprises by improving experts’ confidence in the results of the information system.

RELEVANCE of the study lies in the development of system for the short-term forecasting of power consumption by the enterprise of the oil and gas industry with consideration of technological factors and interpretation of their influence on the result of the forecast.THE PURPOSE. To consider the problems of short-term forecasting. To test the applicability of the multi-agent approach to determine the features used to build a machine learning model of short-term forecasting of power consumption. To build machine learning models. To study the influence of technological factors on the accuracy of forecasting of power consumption. To apply the SHapley Additive exPlanations and analyze its interpretation of the forecasting results.METHODS. Pre-processing of the dataset, construction and testing of machine learning models were made in the programming language Python 3 using opensource libraries Scikit-Learn, XGBoost, LightGBM, Shap.RESULTS. The article describes the relevance of the topic of short-term forecasting of power consumption by the enterprise of the oil and gas industry within the ESG-approach. The method of selecting the features used using a multi-agent approach to build a machine learning model was developed. Machine learning models were built. Experimentations with the consideration of different features were made. Interpretation of results using SHapley Additive exPlanations was made.CONCLUSION. The use of technological factors of power consumption of compressor yards and natural gas air coolers allowed to increase the accuracy of forecast of power consumption from 8.82 % to 3.65 %. The application of the SHapley Additive exPlanations allows to interpret the results of machine learning models and confirms the need to consider technological factors in the task of short-term forecasting of power consumption of oil and gas industry.

This paper presents an analysis of the impact of hyperparameters on the accuracy, stability, and computational efficiency of ensemble models in short-term electrical energy consumption forecasting. The study was conducted on a real compressor station located in northern Central Russia. Experiments were performed using models based on LightGBM, XGBoost, AdaBoost, and Random Forest. The effective tree depth and the number of base learners were selected as hyperparameters, yielding 72 combinations of these parameters for each model. The contribution of this work lies in conducting a full hyperparameter grid search on real data from a gas industry enterprise assessing model tendencies toward overfitting and underfitting by comparing a mean absolute percentage error on training and test sets, and identifying optimal hyperparameter configurations for each model that balance accuracy, speed, and stability. Experimental results show that Random Forest and AdaBoost models demonstrate the highest stability; however, due to long training times and lower accuracy, these models are unsuitable for short-term forecasting in the gas industry applications. XGBoost-based models achieve the best accuracy and predictable error behavior, while LightGBM-based models provide competitive accuracy with the shortest training time.