This thesis deals with the structure and program software of the information and analytical set of tools or assessing efficiency of the civil buildings heating modernization that fully considers the influence factors and allows determining energy-economic decision parameters. The authors suggest using the system of discounting parameters with the limitation of their values for meeting the break-even requirements as an efficiency criterion.

Data analysis and predicting play an important role in managing heat-supplying systems. Applying the models of predicting the systems’ parameters is possible for qualitative management, accepting appropriate decisions relating control that will be aimed at increasing energy efficiency and decreasing the amount of the consumed power source, diagnosing and defining non-typical processes in the functioning of the systems. The article deals with comparing two methods of ma-chine learning: random forest (RF) and support vector machine (SVM) for predicting the temperature of the heat-carrying agent in the heating system based on the data of electronic weather-dependent controller. The authors use the following parameters to compare the models: accuracy, source cost and the opportunity to interpret the results and non-obvious interrelations. The time spent for defining the optimal hyperparameters and conducting the SVM model training is deter-mined to exceed significantly the data of the RF parameter despite the close meanings of the root mean square error (RMSE). The change from 15-min data to once-a-minute ones is done to improve the RF model accuracy. RMSE of the RF model on the test data equals 0.41°С. The article studies the importance of the contribution of variables to the prediction accuracy.

Purpose. Improving the quality of the analysis of energy consumption modes of buildings of educational institutions by determining the typical patterns of their consumption by the k-means method based on statistics of thermal energy consumed, their area, outdoor and indoor air temperature. Methodology. Methods of statistical and intellectual data analysis, optimization methods, k-means method. Findings. It is shown that most of the researched consumers of thermal energy have similar patterns of its consumption and can be divided into separate segments according to the peculiarities of functioning. The used data set is diverse as it includes three groups of data (conditionally formed by the type of educational institutions). The buildings in groups have different areas, internal temperature to be maintained, operation modes of heating systems and so on. For each of the groups, the number of clusters corresponding to low, medium and high values of heat consumption is determined. It was found that the amount of the consumed heat and consumer behavior depends on the day of the week. The obtained results can be difficult to generalize for all existing types of buildings, but it is expected that communal buildings may have similar consumption patterns, as operating modes and norms of internal microclimate remain the same for different temperature zones and territories. Originality. The method for dividing the consumers of thermal energy into segments on the basis of the data on daily energy consumption applying the k-means clustering algorithm, implemented in the programming language R, is further developed. For the first time, it has been proposed to consider not one, but three groups of objects of the educational institutions. It is proposed to determine the modes of energy consumption by buildings analytically by taking into account such parameters as the area of buildings, the amount of heat consumed by them, external and internal air temperature. Practical value. The results of the research are useful for heat supply companies, municipalities and can be used to develop programs and policies for energy efficiency. The given information and analytical support create a basis for the development of software solutions with the function of integration into local energy monitoring systems of a separate building, energy management system of the district and/or city. The presented results provide an opportunity to predict the cost of energy resources used for heating buildings, which increases the efficiency of engineering systems of buildings.

This study addresses the urgent need for advanced digitalization tools in air pollution detection, particularly within resource-constrained municipal settings like those in Ukraine, aligning with directives such as the AAQD. The forecasting information system for integrating data processing, analysis, and visualization to improve environmental monitoring practices is described in this article. The system utilizes machine learning models (ARIMA and BATS) for time series forecasting, automatically selecting the optimal model based on accuracy metrics. Spatial analysis employing inverse distance weighting (IDW) provides insights into pollutant distribution, while correlation analysis identifies relationships between pollutants. The system was tested using retrospective data from the Kremenchuk agglomeration (2007–2024), demonstrating its ability to forecast air quality parameters and identify areas exceeding maximum permissible pollutant concentrations. Results indicate that BATS often outperforms ARIMA for several key pollutants, highlighting the importance of automated model selection. The developed system offers a cost-effective solution for local municipalities, enabling data-driven decision-making, optimized monitoring network placement, and improved alignment with European Union environmental standards.

The paper examines the development of forecasting and modeling technologies for environmental processes using classical and quantum data analysis methods. The main focus is on the integration of deep neural networks and classical algorithms, such as AutoARIMA and BATS, with quantum approaches to improve the accuracy of forecasting environmental parameters. The research is aimed at solving key problems in environmental monitoring, particularly insufficient forecast accuracy and the complexity of processing small data with high discretization. We developed the concept of an adaptive system for predicting environmental conditions in urban agglomerations. Hybrid forecasting methods were proposed, which include the integration of quantum layers in LSTM, Transformer, ARIMA, and other models. Approaches to spatial interpolation of environmental data and the creation of an interactive air pollution simulator based on the A* algorithm and the Gaussian kernel were considered. Experimental results confirmed the effectiveness of the proposed methods. The practical significance lies in the possibility of using the developed models for operational monitoring and forecasting of environmental threats. The results of the work can be applied in environmental information systems to increase the accuracy of forecasts and adaptability to changing environmental conditions.