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  5. VOL 40, NO 2 (2018)
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  7. 2025 год
  8. 47-2

Electronic modeling

Vol 47, No 2 (2025)

CONTENTS

Mathematical modeling and Computation Methods

  S.D. VYNNYCHUK
The Method of Automatic Formation of Defining Nodes for Calculations of Flow Distribution in Hydraulic Networks
3-15
  Z.KH. BORUKAIEV, V.A. EVDOKIMOV, K.B. OSTAPCHENKO
Conceptual Model of the Electricity Micro Market for Solving Demand Management Tasks
16-35
  A.V. STOPKIN
An Algorithm for Recognizing Undirected Graphs by a Multiagent System
36-47
  O.I. KLIUZKO
Model for Forecasting the Volume of Electricity Consumption Using the «Random Forest» Algorithm
48-66

Informational Technologics

 

V. ZALUZHNYI
Troop And Weapon Control Systems: Trends Of Development In The Face Of Modern Armed Conflicts
67-80

Computational Processes and Systems

  A. DAVYDIUK, S. KULYK
Security of Data Migration to the Cloud. Analysis of Challenges and Threats
81-91
  V. KULANOV, A. PEREPELITSYN
Method of Creation of FPGA Projects Using Continuous Integration and Continuous Delivery Technology
92-107

Application of Modeling Methods and Facilities

  Y.V. DOLGIKH

Analysis of the Efficiency of Educational Resources Use by Higher Education Institutions Using the Method of Data Envelopment Analysis
108-126

THE METHOD OF AUTOMATIC FORMATION OF DEFINING NODES FOR CALCULATIONS OF FLOW DISTRIBUTION IN HYDRAULIC NETWORKS

S.D. Vynnychuk

Èlektron. model. 2025, 47(2):03-15

https://doi.org/10.15407/emodel.47.02.003

ABSTRACT

Hydraulic networks are considered, which are characterized by a significant influence of pressure (potential) losses on nodal elements that are tees or are represented by them so that the degree of nodes of the design graph does not exceed 3. The RV algorithm is proposed for the automatic formation of a set of defining nodes, each of which, when broken, ensures the break of two cycles of the system of fundamental cycles of the graph, built on the basis of the system of chords obtained by the method of searching in width when constructing the spanning tree. The RV algorithm proposed for selecting the defining nodes does not break the graph’s connectivity. And in cases where cycles remain after the breaks of the defining nodes, such cycles will be isolated.

KEYWORDS

hydraulic network, flow distribution, calculation methods, determining parameters.

REFERENCES

  1. Yevdokimov A.G., Tevyashev A.D. & Dubrovskiy V.V. (1990). Modelling and optimization of load flow in engineering networks, 2nd ed., revised. and ext., Stroyizdat. 368 p.
  2. Merenkov A.P. and Khasilev V.Ya. (1985). Theory of hydraulic circuits, Nauka. 280 p.
  3. Maksimovich N.G. (1961). Linear Electric Circuits and Their Transformations. Moscow-Leningrad: Gosenergoizdat. 267 p.
  4. Pukhov G.E. (1967). Methods of Analysis and Synthesis of Quasi-Analog Electronic Circuits. Kiev: Nauk. Dumka. 568 p.
  5. Pissaneski S. Sparse Matrix Technology. (1988) / Translated from English. Kh.D. Ikra­mov and E. Kaporin. Ed. Kh.D. Ikramov. Moscow: Mir. 410 p.
  6. Charles F. Van Loan. Matrix Computations. 4. Baltimore: The Johns Hopkins University Press, 2013. 756 p.
  7. Davis T.A. Direct Methods for Sparse Linear Systems (Fundamentals of Algorithms). Society for Industrial and Applied Mathematics, 2006. 218 p.
  8. Maksimovich N.G. (1970). Methods of topological analysis of electrical circuits. Lviv: Lviv. University, 1970. 260 p.
  9. Bun’ R.A., Vasiliev E.D., Semotyuk V.N. (1991). Modeling of electrical circuits by the subcircuit method. / Responsible. editors: Gritsyk V.V.; Academy of Sciences of Ukraine. Physico-Mechanical Institute. Kyiv: Nauk. 176 p.
  10. Vуnnуchuk S.D. Determination of flow distribution in networks with a tree graph. modeling 2016. T. 38, no. 4. pp. 65-80. DOI: https://doi.org/10.15407/emodel.38.04
  11. Vynnychuk S.D. Assigned to the flow subdivision within the prevailing tree-like structure of the graph based on the potential in the middle point of the helic chords. mo­deling 2018. Vol. 40, No. 2. P. 3-16 https://doi.org/10.15407/emodel.40.02.003
  12. Idelchik I.E. (1975) Hydraulic resistances. Moscow: Mashinostroenie. 559 p.
  13. Vynnychuk S.D. Modeling of hydraulic network tees. / Collection of Scientific Works of the IPME NAS of Ukraine. 2001, issue 14. Kyiv: IPME NAS of Ukraine. P. 73-80
  14. Harari F. (1973) Graph Theory. Moscow: Science. 300 p.
  15. Kapitonova Yu.V., Krivy S.L., Letichevsky O.A., Lutsky G.M. (2002) Pechorin M.K. Fundamentals of discrete mathematics. K.: Nauk. Dumka. 580 p.

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CONCEPTUAL MODEL OF THE ELECTRICITY MICRO MARKET FOR SOLVING DEMAND MANAGEMENT TASKS

Z.Kh. Borukaiev, V.A. Evdokimov, K.B. Ostapchenko

Èlektron. model. 2025, 47(2):16-35

https://doi.org/10.15407/emodel.47.02.016

ABSTRACT

The paper presents the results of building a conceptual model of the organization of the Electricity
Micro market at the level of the local electric power system (LES) to determine the conditions
for its creation in solving the problems of electricity demand management (Demand
Response). The difference between the proposed approach to the organization of the Micro
market is to consider it in the context of harmonizing the operation of the centralized electricity
market and the Micro market at the level of the power system under the conditions of implementing
the principles of the Smart Grid concept and Demand Response programs using electricity
generation resources and consumer load. In order to implement the tasks of demand
management, it is proposed to introduce new agents — a demand management aggregator and
participants in the demand management program — into the structural and functional scheme
of the conceptual model of Micro market integration. This conceptual model can be the basis
for the development of a pilot project for the construction of the Micro market in the power
system and the means of mathematical and information technology support for its functioning.

KEYWORDS

local electric power system, micro market, pricing process, electricity market, smart grid, demand side management.

REFERENCES

  1. Directive (EU) 2019/944 of the European Parliament and of the Council of 5 June 2019 on common rules for the internal market for electricity. (2019). https://energysecurityua.org/ua/pereklad-zakonodavstva-es/dyrektyva-yes-2019-944-yevropeyskoho-parlamentu-irady-vid-05-chervnia-2019-roku
  2. Kyrylenko, O.V. (2022). Measures and means of transforming Ukraine’s energy industry into an intelligent, ecologically safe system: Report at the scientific session of the General Assembly of the National Academy of Sciences of Ukraine on February 17, 2022. Bulletin of the National Academy of Sciences of Ukraine. (3). 18-23. https://doi.org/10.15407/visn2022.03.018
  3. Law of Ukraine of 13.04.2017 No. 2019-VIII “On the electricity market”. (2017). https://zakon.rada.gov.ua/laws/show/2019-19#Text
  4. Borukaiev, Z.Kh., Evdokimov, V.A., & Ostapchenko, K.B. (2023). The state and prospects organization of decentralized electricity trade at the regional level. Electronic Modeling. 45(3). 11-27. https://doi.org/10.15407/emodel.45.03.011
  5. Saukh, S.Ye. (2023). Concept of building a structurally variable power system of Ukraine. Tekhnichna Elektrodynamika. (5). 48-54. https://doi.org/10.15407/techned2023.05.048 
  6. Chemerys, O.A. (2019). Tasks of blockchain technology for electric microgrids. Problems of informatization and management. 1(61). 102-107. https://doi.org/10.18372/2073-4751.1.14045
  7. Dudjak, V., Neves, D., Alskaif, T., Khadem, S., Pena-Bello, A., Saggese, P., Bowler, B., Andoni, M., Bertolini, M., Zhou, Y., Lormeteau, B., Mustafa, M.A., Wang, Y., Francis, C., Zobiri, F., Parra, D., & Papaemmanouil, A. (2021). Impact of local energy markets integration in power systems layer: A comprehensive review. Applied Energy. 301. 1-13. https://doi.org/10.1016/j.apenergy.2021.117434
  8. Zahraoui, Y., Korõtko, T., Rosin, A., & Agabus, H. (2023). Market Mechanisms and Trading in Microgrid Local Electricity Markets: A Comprehensive Review. Energie. 16(5), 2145. 1-52. https://doi.org/10.3390/en16052145
  9. Ibekwe, K.I., Ohenhen, P.E., Chidolue, O., Umoh, A.A., Ngozichukwu, B., Ilojianya, V.I., & Fafure, A.V. (2024). Microgrid systems in U.S. energy infrastructure: A comprehensive review: Exploring decentralized energy solutions, their benefits, and challenges inregional implementation. World Journal of Advanced Research and Reviews. 21(01). 973-987. https://doi.org/10.30574/wjarr.2024.21.1.0112
  10. National Energy Company “Ukrenergo”. (2018). The state and prospects of development of technologies of “smart” power grids, demand management and regime control systems in the context of development of renewable energy sources in the foreign energy sector. https://ua.energy/wp-content/uploads/2018/04/1.-Stan-rozvytku-smart-grid.pdf
  11. Stognii, B.S., Kyrylenko, O.V., Prakhovnyk, A.V., & Denysiuk, S.P. (2012). The evolution of intelligent electrical networks and their prospects in Ukraine. Tekhnichna Elektrodynamika. (5). 62-67. https://techned.org.ua/index.php/techned/article/view/1372
  12. Order of the Cabinet of Ministers of Ukraine dated October 14, 2022 No. 908-p “Concept of smart grid implementation in Ukraine by 2035”. (2022). https://zakon.rada.gov.ua/laws/show/908-2022-%D1%80#Text
  13. Resolution of National commission for state regulation of energy and public utilities of March 14, 2018 No. 310 “Code for distribution systems”. (2018). https://zakon.rada.gov.ua/laws/show/v0310874-18#Text
  14. Resolution of National commission for state regulation of energy and public utilities of March 14, 2018 No. 312 “On Approval of the Rules of the Retail Electricity Market”. (2018). https://ips.ligazakon.net/document/GK39809
  15. Концептуальна модель Мікроринку електроенергії для вирішення задач ISSN 0204–3572. Електрон. моделювання. 2025. Т. 47. № 2 35
  16. Borukaiev, Z.Kh., Evdokimov, V.A., & Ostapchenko, K.B. (2023). Construction of the Multi-Agent Environment Architecture of the Pricing Process Simulation Model in the Electricity Market. Electronic Modeling. 45(6). 11-27. https://doi.org/10.15407/emodel.45.06.015

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MODEL FOR FORECASTING THE VOLUME OF ELECTRICITY CONSUMPTION USING THE «RANDOM FOREST» ALGORITHM

O.I. Kliuzko

Èlektron. model. 2025, 47(2):48-66

https://doi.org/10.15407/emodel.47.02.048

ABSTRACT

The article presents the peculiarities of applying the Random Forest (RF) algorithm for short-term forecasting of electricity consumption by consumers served by a supplier company. As a result of processing historical data using the RF algorithm, a forecasting model was developed that takes into account time, meteorological, and calendar features. Identification of the mo­del’s hyperparameters made it possible to achieve high accuracy in forecast calculations. The results of the experimental calculations demonstrate the effectiveness of the model, in particular, the possibility of finding its key qualifying parameters. The features of the model application in the decision-making system of the supplier company regarding the management of energy resources and minimization of the imbalance of electricity volumes in the market are shown.

KEYWORDS

Random Forest; forecasting; electricity consumption; machine learning; energy resources; predictive model.

REFERENCES

  1. Law of Ukraine No. 2019-VIII “On the electricity market”, 13 April 2017
  2. Xuan Y. et al., "Multi-Model Fusion Short-Term Load Forecasting Based on RF Feature Selection and Hybrid Neural Network," in IEEE Access, vol. 9, pp. 69002-69009, 2021, doi: 10.1109/ACCESS.2021.3051337.
  3. Pop, C.B., Chifu, V.R., Cordea, C., Chifu E.S. and Barsan, O. "Forecasting the Short-Term Energy Consumption Using Random Forests and Gradient Boosting," 2021 20th RoEduNet Conference: Networking in Education and Research (RoEduNet), Iasi, Romania, 2021, pp. 1-6, doi: 10.1109/RoEduNet54112.2021.9638276
  4. Lahouar, A. Ben Hadj Slama J. Day-ahead load forecast using random forest and expert input selection, Energy Conversion and Management, Volume 103, 2015, pp 1040-1051, ISSN 0196-8904, https://doi.org/10.1016/j.enconman.2015.07.041.
  5. Pang, X., Luan, C., Liu, L. et al. Data-driven random forest forecasting method of monthly electricity consumption. Electr Eng 104, 2045-2059 (2022). https://doi.org/10.1007/ s00202-021-01457-5
  6. Li, H., Zhou, Q., Tian J. and Lin, X. "Energy Demand Forecasting for an Office Building Based on Random Forests," 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), Wuhan, China, 2020, pp. 29-32, doi: 10.1109/EI250167. 2020.9347021
  7. Rangelov, D., Boerger, M., Tcholtchev, N., Lämmel, P. and Hauswirth, M. "Design and Development of a Short-Term Photovoltaic Power Output Forecasting Method Based on RF, Deep Neural Network and LSTM Using Readily Available Weather Features," in IEEE Access, vol. 11, pp. 41578-41595, 2023, doi: 10.1109/ACCESS.2023.3270714.
  8. Magalhães, B., Bento, P., Pombo, J., Calado, M.d.R., Mariano, S. Short-Term Load Forecasting Based on Optimized Random Forest and Optimal Feature Selection. Energies 2024, 17, 1926. https://doi.org/10.3390/en17081926
  9. Sartini Sartini, Luthfia Rohimah, Yana Iqbal Maulana, Supriatin Supriatin, Dewi Yuliandari Optimization of RF Prediction for Industrial Energy Consumption Using Genetic Algorithms March 2023, PIKSEL Penelitian Ilmu Komputer Sistem Embedded and Lo­gic 11(1):35-44, DOI:10.33558/piksel.v11i1.5886
  10. Resolution of the NCRECP dated 28.12.2018 No. 2118 "On Approval of the Temporary Procedure for Determining Volumes of Electricity Purchases on the Electricity Market by Electricity Suppliers and Distribution System Operators for the Transitional Period" https://zakon.rada.gov.ua/rada/show/v2118874-18#n9
  11. Blinov, I., Parus, E., Klymenko, O., Kliuzko O. The method of comparative evaluations of commercial offers of electricity suppliers for consumers without hourly electricity mete­ring // Power engineering: economics, technique, ecology. 2023. P. 36-42. DOI: https://doi.org/10.20535/1813-5420.3.2023.289654
  12. Rangelov, D., Boerger, M., Tcholtchev, N., Lämmel P. and Hauswirth, M. “Design and Development of a Short-Term Photovoltaic Power Output Forecasting Method Based on RF, Deep Neural Network and LSTM Using Readily Available Weather Features” in IEEE Access, vol. 11, pp. 41578-41595, 2023, doi: 10.1109/ACCESS.2023.3270714.

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TROOP AND WEAPON CONTROL SYSTEMS: TRENDS OF DEVELOPMENT IN THE FACE OF MODERN ARMED CONFLICTS

V. Zaluzhnyi

Èlektron. model. 2025, 47(2):67-80

https://doi.org/10.15407/emodel.47.02.067

ABSTRACT

The peculiarities of command and control of troops and weapons in modern conditions of armed struggle are considered. Based on the analysis of modern armed struggle, the requirements for the integration of distributed information, intelligence, defense, and strike systems on the battlefield are formed, and the key components that affect the achievement of information superiority over the enemy are revealed. The latest technologies that will have an impact on the further development of command and control systems for troops and weapons are identified. The main technological changes that evolutionarily arise as a result of scientific and technological progress are substantiated, as well as trends in the further development of command and control systems for troops and weapons. The relevance of the issue of ensuring the survivability of organizational management systems of forces and means of the Armed Forces of Ukraine is emphasized.

KEYWORDS

artificial intelligence, troop and weapon control systems, situational awareness, survivability.

REFERENCES

  1. Procedure for the implementation of an information security system in state bodies, enterprises, organisations, and in information and communication systems that process information subject to legal protection but not constituting a state secret: ND TZI 3.6-004-21 dated 06.04.2021.
  2. The History of War: Realities and Lessons, Strategy of the Future: A Collection of Military-Historical Essays on Key Operations of the Armed Forces of Ukraine During the Year of the Russian-Ukrainian War (24.02.2022-02.2023) / Center for the Study of Military History of the Armed Forces of Ukraine — Kyiv: Lira-K Publishing, 2023. 131 p.
  3. Weapons of the Russian-Ukrainian War of 2022-2023: A Reference Catalogue of the Main Types of Weapons and Military Equipment Used by Opposing Sides During the Repulsion of the Large-Scale Invasion of the Russian Federation into Ukraine (24.02.2022-06.2023) / Center for the Study of Military History of the Armed Forces of Ukraine – Kyiv: Lira-K Publishing, 2023. 243 p.
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