INTEGRATION OF EXPERT SYSTEMS INTO THE ENERGY SECTOR ON THE BASIS OF UNIVERSITY KNOWLEDGE BASES

Authors

  • Ігор Чудик Ivano-Frankivsk National Technical University of Oil and Gas
  • Даріуш Сала Ivano-Frankivsk National Technical University of Oil and Gas
  • Алла Полянська Ivano-Frankivsk National Technical University of Oil and Gas
  • Святослав Ярославович Кісь ІФНТУНГ

DOI:

https://doi.org/10.31471/2409-0948-2025-1(31)-11-25

Keywords:

expert systems, library, university knowledge base, integration, energy sector

Abstract

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.

References

Ivano-Frankivsk National Technical University of Oil and Gas. (2024). Strategiia do 2028 roku “Talanty dlia naftohazovoi haluzi ta enerhetychnoho perekhodu Ukrainy” [Strategy to 2028: Talents for the Oil and Gas Industry and Ukraine’s Energy Transition]. https://nung.edu.ua/sites/default/files/2024-01/Стратегія%202024_2028.pdf (in Ukrainian)

Chudyk, I., Dmytruk, V., Humeniuk, V., Polianska, A., & Zapuchliak, I. (2024). Information support of value-based management of oil and gas enterprises. Science and Innovation, 20(4), 70–80. https://doi.org/10.15407/scine20.04.070

Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.

Giarratano, J., & Riley, G. (2005). Expert systems: Principles and programming (4th ed.). Thomson.

Jackson, P. (1998). Introduction to expert systems (3rd ed.). Addison Wesley.

Yang, X., & Zhu, C. (2024). Industrial expert systems review: A comprehensive analysis of typical applications. IEEE Access, 12. https://doi.org/10.1109/ACCESS.2024.3419047

Rao, S. S., Nahm, A., Shi, Z., Deng, X., & Syamil, A. (1999). Artificial intelligence and expert systems applications in new product development – A survey. Journal of Intelligent Manufacturing, 10, 231–244. https://doi.org/10.1023/A:1008943723141

Turban, E., & Watson, H. (2014). Integrating expert systems, executive information systems, and decision support systems. In Proceedings. https://www.researchgate.net/publication/249913511

Černevičienė, J., & Kabašinskas, A. (2022). Review of multi-criteria decision-making methods in finance using explainable artificial intelligence. Frontiers in Artificial Intelligence, 5, Article 827584. https://doi.org/10.3389/frai.2022.827584

Liebowitz, J. (1995). Expert systems: A short introduction. Engineering Fracture Mechanics, 50(5–6), 601–607. https://doi.org/10.1016/0013-7944(94)E0047-K

Liao, S.-H. (2005). Expert system methodologies and applications – A decade review from 1995 to 2004. Expert Systems with Applications, 28(1), 93–103. https://doi.org/10.1016/j.eswa.2004.08.003

Gwendo, J. O. (2023). Trends and insights on tools used for the development of expert systems: A systematic review of research articles (2018–2022). International Journal of Engineering Applied Sciences and Technology, 8(01), 331–347. https://doi.org/10.33564/IJEAST.2023.v08i01.050

Khosravi, H., Sadiq, S., & Amer-Yahia, S. (2023). Data management of AI-powered education technologies: Challenges and opportunities. Learning Letters, 1, Article 2. https://doi.org/10.59453//XLUD7002

Sukhodolia, O. M. (2022). Shtuchnyi intelekt v enerhetytsi: Analitychna dopovid [Artificial intelligence in the energy sector: Analytical report]. Kyiv: NISD. https://doi.org/10.53679/NISS-analytrep.2022.09 (in Ukrainian)

Polyanska, A. (2024). Application of decision support systems (DSS): The case of energy company. In Proceedings of the 44th IBIMA Conference: Empower business and create economic development in digital future – Vision 2030 (pp. 1385–1393). IBIMA. https://u.pcloud.link/publink/show?code=cLzrtalK

Makoiedova, V. O. (2024). Intellectual systems in the energy sector. Investments: Practice and Experience, (22), 101–105. https://doi.org/10.32702/2306-6814.2024.22.101

Buchert, T., Ko, N., Graf, R., Vollmer, T., Alkhayat, M., Brandenburg, E., Stark, R., Klocke, F., Leistner, P., & Schleifenbaum, J. H. (2019). Increasing resource efficiency with an engineering decision support system for comparison of product design variants. Journal of Cleaner Production, 210, 1051–1062. https://doi.org/10.1016/j.jclepro.2018.11.104

Azimi, R., Ghayekhloo, M., & Ghofrani, M. (2016). A hybrid method based on a new clustering technique and multilayer perceptron neural networks for hourly solar radiation forecasting. Energy Conversion and Management, 118, 331–344.

Gnatiuk, S., Sakovich, L., Kuriata, Ya., Semekha, S., Dybach, O., & Honchar, S. (2025). Ensuring the necessary level of reliability for electronic means in I&C systems. Nuclear and Radiation Safety, (1)(105). https://doi.org/10.32918/nrs.2025.1(105).02

Ioshchikhes, B., & Weigold, M. (2024). Development of stationary expert systems for improving energy efficiency in manufacturing. Procedia CIRP, 126, 921–926. https://doi.org/10.1016/j.procir.2024.08.351

Okunlaya, R. O., Syed Abdullah, N., & Alias, R. A. (2022). Artificial intelligence (AI) library services innovative conceptual framework for the digital transformation of university education. Library Hi Tech, 40(6), 1869–1892. https://doi.org/10.1108/LHT-07-2021-0242

Denbnovetskyi, S. O. (2022). Digital transformation of Ukrainian libraries in the context of globalization processes. Library Science. Document Science. Informology, (1), 26–33. http://nbuv.gov.ua/UJRN/bdi_2022_1_6 (in Ukrainian)

Kuzmenko, O. I., & Zahumenna, V. V. (2021). Transformation and expansion of library functions in the modern digital space. Library Science. Document Science. Informology, (3), 38–44. http://nbuv.gov.ua/UJRN/bdi_2021_3_7 (in Ukrainian)

Mikhalevska, H. I., & Mikhalevskyi, V. Ts. (2010). Basic concepts of building expert systems. Scientific Papers of the Faculty of Applied Mathematics and Computer Technologies of Khmelnytskyi National University, 1(3). http://ap.khnu.km.ua/articles/files/. (in Ukrainian)

Karake, Z. A. (1990). Enhancing the learning process with expert systems. Computers & Education, 14(6), 495–503. https://doi.org/10.1016/0360-1315(90)90108-J

Bieda, B., Sala, D., Polyanska, A., Babets, D., & Dychkovskyi, R. (2023). Knowledge management in organisation on the base of using the hybrid methods of uncertainty analysis. Scientific Papers of Silesian University of Technology, 187, 55–74. https://doi.org/10.29119/1641-3466.2023.187.3

Ernest, E. (1987). Expert systems and document handling. Information Processing and Management, 23(2), 77–80.

Kappes, E. P. (1990). A study of the uses of expert systems in the training environment (Master’s thesis). Old Dominion University. https://digitalcommons.odu.edu/ots_masters_projects/415

Khosravi, H., Buckingham Shum, S., Chen, G., Conati, C., Tsai, Y.-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gašević, D. (2022). Explainable Artificial Intelligence in education. Computers and Education: Artificial Intelligence, 3, 100074. https://doi.org/10.1016/j.caeai.2022.100074

Published

2025-06-30

How to Cite

Чудик , І., Сала, Д., Полянська , А., & Кісь, С. Я. (2025). INTEGRATION OF EXPERT SYSTEMS INTO THE ENERGY SECTOR ON THE BASIS OF UNIVERSITY KNOWLEDGE BASES. Scientific Bulletin of Ivano-Frankivsk National Technical University of Oil and Gas (Series: Economics and Management in the Oil and Gas Industry), (1(31), 11–25. https://doi.org/10.31471/2409-0948-2025-1(31)-11-25

Most read articles by the same author(s)