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 chapter discusses the problem of energy management in Smart MicroGrid. The strategies of Smart MicroGrid energy management and objectives of Smart MicroGrid operation have been analyzed. The chapter emphasizes the potential of information technologies implementation to achieve energy management goals and provide a description of energy management information system which is used for MicroGrid planning and operation. The information flows which are used for making decision on Smart MicroGrid energy management have been analyzed.

The purpose of this study is a solving the multicriteria task of choosing the optimal structure of a hybrid energy system with renewable sources. To achieve this goal the models for making decision on determination of all possible configurations of energy system and a selection of the optimal structure of its elements for each of the configuration were proposed. The proposed model differs from the existing ones by the ability to generate all possible configurations of the energy system depending on existing on the market installations of renewable energy sources.