Fractal-cluster technology for assessing the reliability of the information potential and digital entropy parameters of energy companies

Budanov O. P.

Article

Issue:	2025 №3 (91)
Section:	Economics of enterprise
UDK:	004.9:519.7:005.8
DOI:	https://doi.org/10.33271/ebdut/91.176
Article language:	Ukrainian
Pages:	176-187
Title:	Fractal-cluster technology for assessing the reliability of the information potential and digital entropy parameters of energy companies
Author:	Budanov O. P., V. N. Karazin Kharkiv National University
Annotation:	Methods. The results were obtained using the following methods: fractal-cluster modeling – to identify hierarchical relationships between information resources and their impact on enterprise sustainability; multidimensional statistics methods – to group indicators and identify hidden dependencies; scenario modeling – to predict the impact of digital entropy on the functioning of an energy enterprise in a coherent digital environment. Results. In the context of the digital transformation of the economy, a key challenge for enterprises is to ensure the reliability and consistency of information flows, which directly affects the quality of management decisions and the sustainability of business processes. The problem lies in the lack of effective tools for a comprehensive assessment of the reliability of the information potential of energy companies in the context of growing digital entropy and the need to ensure information coherence. It is shown that the growth of digital entropy leads to data chaos, complicates their verification and integration into a single information space, which is especially critical for energy companies with a high level of technological complexity. Novelty. A fractal-cluster technology for assessing the reliability of information potential is proposed, combining fractal analysis methods and clustering algorithms. Fractal dimension is used as a quantitative indicator of structural complexity and the level of hidden chaos in information data, while the cluster approach provides systematic grouping of information components according to the criteria of completeness, accuracy, relevance, and coherence. This allows for the formation of an integral indicator of the reliability of an enterprise's information potential. A methodology for determining digital entropy parameters is proposed, which makes it possible to establish a relationship between the level of information chaos and the digital coherence of an enterprise. The scientific novelty of the work lies in the formation of a universal tool for assessing the reliability of the information potential and parameters of digital entropy of energy enterprises, which integrates quantitative and qualitative characteristics into a single analytical space and can be used both for strategic monitoring and for operational management of the information potential of energy enterprises. Practical value. Practical testing on energy companies showed that using a fractal-cluster approach makes the diagnosis of unreliable data more accurate, helps in lowering digital entropy, optimizes information resources, and boosts a company's digital coherence. The results obtained are of practical importance for the formation of information security systems, improving management efficiency, and ensuring the digital resilience of enterprises in conditions of digital entropy.
Keywords:	Methodology, Information potential, Energy enterprises, Digital technologies, Digital entropy, Digital coherence, Management, Fractal-cluster technology, Information technologies, Digital transformation
File of the article:	EV20253_176-187.pdf
Literature:	1. Prokhorova, V.V. (Ed.). (2024). Transformatsiia ekonomichnoho seredovyshcha vumovakh entropii. Kharkiv: Vydavnytstvo Ivanchenka I.S. Retrieved from https://crust.ust.edu.ua/server/api/core/bitstreams/2486eb3a-943c-4608-a987-0b645aea8f1d/content 2. Prokhorova, V., & Budanov, M. (2024). Entropy as a factor of influence on energy security management of enterprises. Technology Audit and Production Reserves, 5(4(79), 6-12. https://doi.org/10.15587/2706-5448.2024.314397 3. Prokhorova, V., Budanov, M., & Budanov, P. (2024). Devising an integrated methodology for energy safety assessment at an industrial power-generating enterprise. Transfer of Technologies: Industry, Energy, Nanotechnology, 4 (13 (130)), 118-131. https://doi.org/10.15587/1729-4061.2024.3080 4. Riepina, I., Tepliuk, M., Dziuba, D., & Moroz, A. (2024). The influence of entropy and digitalization on the development of enterprises. Development Service Industry Management, (2), 238-243. https://doi.org/10.31891/dsim-2024-6(37) 5. Riepina, I., Tepliuk, M., & Dziuba, D. (2024). Vplyv entropiinykh protsesiv na hlokalizatsiinyi rozvytok pidpryiemstv v umovakh tsyfrovizatsii. Modeling the development of the economic systems, (2), 229-234. https://doi.org/10.31891/mdes/2024-12-30 6. Tepliuk, M. (2024). The genesis of entropy in the conditions of the digital economy and socio-economic relations: globalization challenges and opportunities for sustainable development. Collection of Scientific Papers «Scientific Notes», 36(3), 149-159. http://doi.org/10.33111/vz_kneu.36.24.03.14.096.102 7. Bigdan, A., Babenko, T., Hnatiienko, H., Baranovskyi, O., & Myrutenko, L. (2022). Detection of Cybersecurity Events Based on Entropy Analysis. Pro- ceedings from MIIM ’22 7th International Conference on Digital Technologies in Education, Science and Industry, DTESI 2022, Almaty, 20 October through 21 October 2022. Technical University of Aachen. https://ceur-ws.org/Vol-3382/Paper21.pdf 8. Yehorova-Hudkova, T. (2020). Deiaki metodolohichni skladovi pryrodopodobnoho upravlinnia ta ekonomichna bezpeka derzhavy. Ekonomika ta suspilstvo, (22). https://doi.org/10.32782/2524-0072/2020-22-69 9. Pushkar, O. (2025). Modeliuvannia rozvytku informatsiinykh system v ekonomitsi. Herald of Khmelnytskyi National University. Economic Sciences,. (2), 555-560. DOI: https://doi.org/10.31891/2307-5740-2025-340-88 10. Tereshchenko, L.O. (2021). Tekhnolohii modeliuvannia upravlinskykh informatsiinykh system v systemi menedzhmentu pidpryiemstva. Ekonomika i suspilstvo, Vyp 27. DOI: https://doi.org/10.32782/2524-0072/2021-27-55 11. Shevchenko, S., Zhdanova Y., Spasiteleva, S., Negodenko, O., Mazur, N., & Kravchuk, K. (2019). Matematychni metody v kiberbezpetsi: fraktaly ta yikh zastosuvannia v informatsiinii ta kibernetychnii bezpetsi. Elektronne fakhove naukove vydannia «Kiberbezpeka: osvita, nauka, tekhnika», 1(5), 31-39. https://doi.org/10.28925/2663-4023.2019.5.3139 12. Tur H.I., & Trunova, O.V. (2015). Zastosuvannya metodu fraktalʹnoho analizu dlya vyznachennya trendovykh kharakterystyk chyslovykh ryadiv. [Application of fractal analysis method for determination of trending characteristics of numerical series], Bulletin of Chernihiv National Pedagogical University. Series: Pedagogical Sciences, vol. 125, pp. 252-256, 2015. [Online] Retrieved from http://nbuv.gov.ua/UJRN/VchdpuP_2015_125_61 [Aug 13 2019] (in Ukrainian). 13. M.A. Nazarkevich, M.A., Dronyuk, I.M., OA Troyan, O.A., & Tomaschuk, T.Yu. (2015). Rozrobka metodu zakhystu dokumentiv latentnymy elementamy na osnovi fraktaliv. [Developing a Method for Securing Documents with Fractal-Based Latent Elements] Information Security, Volume 17, No. 1, pp. 21-26. [Online] Retrieved from http://jrnl.nau.edu.ua/index.php/ZI/article/view/7505/9882 (in Ukrainian). 14. Janani, K. (2025). Cybersecurity through Entropy Injection: A Paradigm Shift from Reactive Defense to Proactive Uncertainty. arXiv preprint arXiv:2504.11661. 15. Al-Zoubi, W.K. (2024). Economic Development in the Digital Economy: A Bibliometric Review. Economies, 12(3), 53. https://doi.org/10.3390/economies12030053 16. Yehorova-Hudkova, T. (2020). Deiaki metodolohichni skladovi pryrodopodobnoho upravlinnia ta ekonomichna bezpeka derzhavy. Ekonomika ta suspilstvo, (22). https://doi.org/10.32782/2524-0072/2020-22-69 17. Zhao, T., Li, Z., & Deng, Y. (2023). Information fractal dimension of random permutation set. Chaos, solitons & fractals, 174, 113883. https://doi.org/10.1016/j.chaos.2023.113883 18. Qiang, C., Deng, Y., & Cheong, K. H. (2022). Information fractal dimension of mass function. Fractals, 30(06), 2250110. https://doi.org/10.1142/S0218348X22501109 19. Ji, X., Henriques, J. F., & Vedaldi, A. (2019). Invariant information clustering for unsupervised image classification and segmentation. Proceedings from MIIM ’19 The IEEE/CVF International conference on computer vision. (pp. 9865-9874). 20. Bhavsar, R., Helian, N., Sun, Y., Davey, N., Steffert, T., & Mayor, D. (2018). Efficient Methods for Calculating Sample Entropy in Time Series Data Analysis. Procedia Computer Science, 145, 97-104. https://doi.org/10.1016/j.procs.2018.11.0162. 21. Chen, C., Sun, S., Cao, Z., Shi, Y., Sun, B., & Zhang, X.D. (2019). A comprehensive comparison and overview of R packages for calculating sample entropy. Biology Methods and Protocols, 4(1). https://doi.org/10.1093/biomethods/bpz0164 22. Dippo, O.F., & Vecchio, K.S. (2021). A universal configurational entropy metric for high-entropy materials. Scripta Materialia, 201, 113974. https://doi.org/10.1016/j.scriptamat.2021.1139745 23. Asenjo, D., Paillusson, F., & Frenkel, D. (2014). Numerical Calculation of Granular Entropy. Physical Review Letters, 112 (9), 098002. https://doi.org/10.1103/physrevlett.112.0980026 24. Davies, S. R., Macfarlane, R., Buchanan, W. J. (2022). Comparison of Entropy Calculation Methods for Ransomware Encrypted File Identification. Entropy, 24 (10), 1503. https://doi.org/10.3390/e241015037 25. Heidari, H., Velichko, A., Murugappan, M., & Chowdhury, M. E. H. (2023). Novel techniques for improving NNetEn entropy calcula-tion for short and noisy time series. Nonlinear Dynamics, 111 (10), 9305-9326. https://doi.org/10.1007/s11071-023-08298-w8 26. Velichko, A., & Heidari, H. (2021). A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks. Entropy, 23 (11), 1432. https://doi.org/10.3390/e231114329 27. Sherwin, W. B., & Prat i Fornells, N. (2019). The Introduction of Entropy and Information Methods to Ecology by Ramon Margalef. Entropy, 21 (8), 794. https://doi.org/10.3390/e2108079410 28. Ekberg, V., & Ryde, U. (2021). On the Use of Interaction Entropy and Related Methods to Estimate Binding Entropies. Journal of Chemical Theory and Computation, 17 (8), 5379-5391. https://doi.org/10.1021/acs.jctc.1c003743.