Forecasting electricity consumption is one of the most important scientific and practical tasks in the field of electric power engineering. The forecast accuracy directly impacts the operational efficiency of the entire power system and the performance of electricity markets. This paper proposes algorithms for source data preprocessing and tuning XGBoost models to obtain the most accurate forecast profiles. The initial data included hourly electricity consumption volumes and meteorological conditions in the power system of the Republic of Tatarstan for the period from 2013 to 2025. The novelty of the study lies in defining and justifying the optimal model training period and developing a new evaluation metric for assessing model efficiency—financial losses in Balancing Energy Market operations. It was shown that the optimal depth of the training dataset is 10 years. It was also demonstrated that the use of traditional metrics (MAE, MAPE, MSE, etc.) as loss functions during training does not always yield the most effective model for market conditions. The MAPE, MAE, and financial loss values for the most accurate model, evaluated on validation data from the first 5 months of 2025, were 1.411%, 38.487 MWh, and 16,726,062 RUR, respectively. Meanwhile, the metrics for the most commercially effective model were 1.464%, 39.912 MWh, and 15,961,596 RUR, respectively.

A solid oxide fuel cell (SOFC) is an electrochemical energy conversion device that provides higher thermoelectric efficiency than traditional cogeneration systems. Current research in this field highlights a variety of mathematical models. These models are based on complex physicochemical and electrochemical reactions, enabling accurate simulation and optimal control of fuel cells. However, these models require substantial computational resources, leading to high processing times. White box and gray box models are unable to achieve real-time optimization of control parameters. A potential solution involves using data-driven machine learning (ML) black-box models. This study examines three ML models: artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGB). The training dataset consisted of experimental results from SOFC laboratory experiments, comprising 32,843 records with 47 control parameters. The study evaluated the effectiveness of input matrix dimensionality reduction using the following feature importance evaluation methods: mean decrease in impurity (MDI), permutation importance (PI), principal component analysis (PCA), and Shapley additive explanations (SHAP). The application of ML models revealed a complex nonlinear relationship between the SOFC output voltage and the control parameters of the system. The default XGB model achieved the optimal balance between accuracy (MSE = 0.9940) and training speed (τ = 0.173 s/it), with performance capabilities that enable real-time enhancement of SOFC thermoelectric characteristics during system operation.

At thermal power plants, installations in contact with the water coolant are subject to biological contamination. This is due to a number of factors: the maintenance of favorable temperature regimes, the formation of stagnant zones, the constant supply of nutrients, the presence of large areas of equipment surfaces (pipelines, water storage tanks, the pipe surface of the condenser), the presence of various materials (ion-exchange resins, membranes), and changing climatic conditions. The fight against the formation of such pollution of thermal power equipment is very relevant today, although almost all TPPs carry out thorough preparation of additional and feed water. It should be noted that the control of the concentration of organic impurities in the liquid and vapor phases, as well as continuous monitoring, are rather laborious processes. Organic deposits and the presence of biofilms on the process equipment of circulating cooling systems (CCS), water treatment plants (WTP), and chemically treated water storage tanks lead to various production failures, emergencies, and a general decrease in the efficiency of heat and electricity generation. In this paper, foreign and domestic studies on the features of the formation and development of biofilms were reviewed. Current methods for detecting and assessing biological pollution are considered and traditional chemical, physical, electrochemical, acoustic, electromagnetic, and other methods of combating microorganisms and bacteria are described. It has been shown that the growth of bacteria significantly complicates the equipment-cleaning procedures and accelerates the process of scale formation. To effectively solve the problems of biological deposits, the development of methods for monitoring and controlling the formation of bacterial deposits, the preparation of additional water, and the maintenance of a water-chemical regime must be carried out differentially based on the identification of colonies of microorganisms using test systems. The previous works of the team of authors concerning the issues of pollution of the coolant of circulating cooling systems and water treatment plants at TPPs of the Republic of Tatarstan in the period from 2009 to 2022 are noted.

The correct prediction of heating network pipeline failure rates can increase the reliability of the heat supply to consumers in the cold season. However, due to the large number of factors affecting the corrosion of underground steel pipelines, it is difficult to achieve high prediction accuracy. The purpose of this study is to identify connections between the failure rate of heating network pipelines and factors not taken into account in traditional methods, such as residual pipeline wall thickness, soil corrosion activity, previous incidents on the pipeline section, flooding (traces of flooding) of the channel, and intersections with communications. To achieve this goal, the following machine learning algorithms were used: random forest, gradient boosting, support vector machines, and artificial neural networks (multilayer perceptron). The data were collected on incidents related to the breakdown of heating network pipelines in the cities of Kazan and Ulyanovsk. Based on these data, four intelligent models have been developed. The accuracy of the models was compared. The best result was obtained for the gradient boosting regression tree, as follows: MSE = 0.00719, MAE = 0.0682, and MAPE = 0.06069. The feature «Previous incidents on the pipeline section» was excluded from the training set as the least significant.