INTEGRATION OF EXPERT SYSTEMS INTO THE ENERGY SECTOR ON THE BASIS OF UNIVERSITY KNOWLEDGE BASES - Чудик , Ігор; Сала, Даріуш; Полянська , Алла; Кісь, Святослав Ярославович (Scientific Bulletin of Ivano-Frankivsk National Technical University of Oil and Gas (Series: Economics and Management in the Oil and Gas Industry), 2025)
The article presents a conceptual model of an expert system that integrates the knowledge potential of university electronic resources with the practical needs of the energy sector. The potential of university libraries as sources of formalized knowledge for the creation of intelligent expert systems is studied. In accordance with the goal and objectives of the article, an analysis of modern approaches to building knowledge bases focused on application in energy is conducted; a model of the architecture of an expert system with library resources as a source of updated data and knowledge formation is formed. The components of this system are summarized, which consist of a university infrastructure, which is the basic source of updated knowledge for the creation of expert systems; a mechanism for building a knowledge base that formalizes available knowledge and the expert system itself based on consideration of methods of integrating knowledge in an expert system. The scenarios of application of expert systems in energy sub-sectors are considered. The article assesses the advantages and limitations of integration in terms of technological, organizational and academic interaction. Recommendations are proposed for the implementation of an expert system based on a university in partnership with stakeholders. It is concluded that such integration increases the accuracy of analytical solutions, creates a dynamic knowledge base and uses the existing infrastructure of higher education for the rapid deployment of adaptive solutions. It is generalized that the social partnership between the academy and energy enterprises is a critical factor in the success of the model. Promising directions are the improvement of semantic search with NLP, integration with real-time energy monitoring platforms, development of self-learning modules and assessment of the economic efficiency of implementation. The proposed model can serve as a tool for implementing the development strategy of IFNTUNG, as well as the basis for the transformation of the domestic energy sector in accordance with the principles of sustainable development and the digital economy of the European Green Deal.