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  5. VOL 40, NO 2 (2018)
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  7. 2022 год
  8. 44-3

Electronic modeling

Vol 44, No 3 (2022)

CONTENTS

Computatinal Processes and Systems

 
SAUKH S.Ye., DZHYHUN O.M.
Modeling of Regional Development of WPP and SES Capacities Under Different Scenario Conditions of Long-Term Development of Electric Power Industry of Ukraine
3-13
 
KRAVTSOV H.O., HRECHKO S.M., NIKITCHENKO V.V., PRYMUSHKO A.M.
Cognitive Algebraic System
14-30
 
CHERNOZOMOV Y.S.
Cobaltic Hyperboloid. Models of Energy Reflection in the Region of Normal Incidence Angles
31-41
 
ROGOZA W., ISHCHENKO G.
Comparative Properties of Deterministic Methods of Time Series Forecasting on Small Sets of Samples
  
42-49

Informational technologics

 
KAMENEVA I.P., ARTEMCHUK V.O.
The Problem of Informativeness and Identification of Informative Structures to Support Decision-Making in the Field of Environmental Safety
50-64

Application of Modeling Methods and Facilities

 
ZUBOK V., HONCHAR S., YERMOSHYN V., KARASIUK G.
Architectural and Functional Comparison of known Platforms and Cybersecurity Systems of Industrial Facilities
65-86
 
VLADIMIRSKY A.A., VLADIMIRSKY I.A.
Review of Modern Methods and Tools for Remote Corrosion Acoustic Monitoring of Underground Pipelines
87-100
 
OSTAPCHENKO K.B., EVDOKIMOV V.A., BORUKAIEV Z.Kh.
Operational Data Warehouseof the Decision Support System for Organizational Management of the Electricity Market
101-112
 
SMIRNOVA T.V., YANKOV M.A., HRUDYK V.V., HORBOV V.A., KOVALENKO A.S.
Planning of Radio Coverage and Modeling of Distribution of Radio Signals of 5G Mobile Networks for Automation of Production Processes
113-122

MODELING OF REGIONAL DEVELOPMENT OF WPP AND SES CAPACITIES UNDER DIFFERENT SCENARIO CONDITIONS OF LONG-TERM DEVELOPMENT OF ELECTRIC POWER INDUSTRY OF UKRAINE

S.Ye. Saukh, O.M. Dzhyhun

Èlektron. model. 2022, 44(3):03-13

https://doi.org/10.15407/emodel.44.03.003

ABSTRACT

The analysis of various strategies of development of generating capacities which use RES is carried out. It is shown that the forecast indicators of electricity production from RES and the installed capacity of the respective generating units are system-wide and do not reflect the peculiarities of their regional development. It is noted that the regional development of capacities that use RES leads to the formation of "smart" networks, i.e. such participants in the electricity market that are able to form their own schedules of production and consumption of electricity in the power system. To reflect the peculiarities of the functioning of  "smart" networks in the UES of Ukraine, the problem of modeling the regional distribution of system-wide volumes of electricity production from RES, in particular, generating equipment for wind farms and SES, is formulated and solved. The problem takes into account regional differences in the efficiency of generating equipment of wind farms and wind farms, the pace of socio-economic development of regions, the maximum achievable values of electricity production and wind farms in regional energy systems of Ukraine, as well as the already established capacity and electricity production. SES in these regional energy systems in the base year. The results of modeling the regional distribution of total electricity production of wind farms and wind farms for the long term in two possible scenarios are presented.

KEYWORDS

renewable energy sources, modeling, regional development.

REFERENCES

  1. Law of Ukraine "On the electricity market" of 13.04.2017 № 2019-VIII, available at: http://search.ligazakon.ua/l_doc2.nsf/link1/T190330.html.  
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  3. Ukraine's transition to renewable energy by 2050. Report on the results of modeling the baseline and alternative scenarios for the development of the energy sector, available at: https://ua.boell.org/sites/default/files/perehid_ukraini_na_vidnovlyuvanu_energetiku_ do_ 2050_roku.pdf.
  4. REMAP—2030 (2015), Prospects for the development of renewable energy in Ukraine until 2030, Kyiv, Ukraine, available at: https://saee.gov.ua/sites/default/files/UKR% 20IRENA%20REMAP%20_%202015.pdf.
  5. Guidelines for the development of alternative energy in Ukraine until 2030, available at: https://razumkov.org.ua/statti/oriientyry-rozvytku-alternatyvnoi-energetyky-ukrainy-do- 2030r.
  6. Long-term energy modeling and forecasting in Ukraine: scenarios for the action plan for the implementation of the Energy Strategy of Ukraine for the period up to 2035. Final Kyiv-Copenhagen, 2019, available at: https://ens.dk/sites/ens.dk/files/Globalcooperation/longterm_energy_modelling_and_forecasting_in_ukraine_ukrainian.p.
  7. Analytical review of the updated nationally determined contribution of Ukraine to the Paris Agreement, available at: https://mepr.gov.ua/files/docs/klimatychna_polityka.
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  12. Informatsiya pro obyekty alternatyvnoyi enerhetyky, yakym vstanovleno "zelenyy" taryf [Information on alternative energy facilities with a "green" tariff] (2021), National Commission for State Regulation of Energy and Utilities, available at: http://www.nerc.gov.ua/data/filearch/elektro/energo_pidpryemstva/stat_info_zelenyi_taryf/2018/stat_zelenyi-taryf.12-2018.pdf.

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COGNITIVE ALGEBRAIC SYSTEM

H.O. Kravtsov, S.M. Hrechko, V.V. Nikitchenko, A.M. Prymushko

Èlektron. model. 2022, 44(3):14-30

https://doi.org/10.15407/emodel.44.03.014

ABSTRACT

Authors propose an algebraic system with some special axioms as a mathematical framework to model arbitrary cognitive agents. We develop cognitive process as the composition of functions that happens if and only if some given requirements are met with some given probability; we introduce definitions of objective and subjective contradiction within a cognitive algebraic system (CAS). We proved that subjective contradiction makes CAS unable to find an optimal solution analytically and thus such a CAS is deliberated to fallback to the combinatorics and its methodology. Rigorous definitions of theoretical and practical experimentation and theoretical and practical learning were given, also was shown the role of (natural) language in these processes. New science problem arose — the problem of defining language’s semantics within described CAS.

KEYWORDS

strong artificial intelligence, cognitive algebraic system, consistency of cognitive system, time quantum, nature of subjective time, synthesis model, learning, research.

REFERENCES

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    https://doi.org/10.15407/emodel.40.03.063
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    https://doi.org/10.15407/emodel.43.03.087
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    https://doi.org/10.15407/emodel.43.04.069

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COBALTIC HYPERBOLOID. MODELS OF ENERGY REFLECTION IN THE REGION OF NORMAL INCIDENCE ANGLES

Y.S. Chernozomov

Èlektron. model. 2022, 44(3):31-41

https://doi.org/10.15407/emodel.44.03.031

ABSTRACT

The features of the reflection of unpolarized solar radiation in the infrared region of the spectrum, in which the heating of reflecting surfaces occurs, are considered. Mathematical models of the angular dependences of the reflection of a p-polarized wave in the region of normal incidence angles are presented. An optical system of a solar energy concentrator and a system for transmitting a high-potential ray flux with a bandwidth of the energy component of solar radiation are proposed.

KEYWORDS

angle of normal incidence, grazing incidence angle, infrared spectrum, polarization conservation region, energy component of radiation, optical anisotropy, Brews­ter angle.

REFERENCES

  1. Chernozоmov, E.S. (2020), “Models of energy distribution at the interface of media in dense energy fields of a solar concentrator system”, Elektronne modelyuvannya, Vol. 42, no. 6, pp. 34-55.
    https://doi.org/10.15407/emodel.42.06.034
  2. Chernozоmov, E.S. (2021), “Models of radiation polarization in the solar energy concentrator system”, Elektronne modelyuvannya, Vol. 43, no. 5, pp. 93-107.
    https://doi.org/10.15407/emodel.43.05.093
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  4. Libenson, M.N., Yakovlev, E.B. and Shandybina, G.D. (2008), Vzaimodeystviye lazernogo izlucheniya s veshchestvom (silovaya optika). Chast I. Pogloshcheniye lazernogo izlucheniya v veshchestve [Interaction of laser radiation with matter (power optics). Part I. Absorption of laser radiation in matter], Universitet ITMO, St. Petersburg, Russia.
  5. Veiko, V.P., Libenson, M.N., Chervyakov, G.G. and Yakovlev, E.B. (2008), Vzaimodeistviye lazernogo izlucheniya s veshchestvom. Silovaya optika [Interaction of laser radiation with matter. Power optics], FIZMATLIT, Moscow, Russia.
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  17. Chernozоmov, E.S. (2021), Patent for utility model 149777, Ukraine, IPC (2021.01) F24S 10/00, F24S 20/00, F24S 23/00, G02B 6/00 “Concentrator-collimator of solar radiation based on hyperboloid”, 04520; application date August 04, 2021; publication date December 01, 2021; Bulletin no. 48.
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COMPARATIVE PROPERTIES OF DETERMINISTIC METHODS OF TIME SERIES FORECASTING ON SMALL SETS OF SAMPLES

W. Rogoza, G. Ishchenko

Èlektron. model. 2022, 44(3):42-49

https://doi.org/10.15407/emodel.44.03.042

ABSTRACT

A comparative analysis of two deterministic methods of short-term forecasting of time series in the conditions of a limited amount of experimental data is performed. The first method is based on the construction of so-called partial predictive models in the form of Kolmogorov-Gabor second-order polynomials. In the second method, in addition to these polynomials, predictive partial models are built in the form of increments of changes in the parameters of the object under study. This makes it possible to increase the amount of data for training forecast models. Due to this increase, the researcher gets more accurate predictions for each parameter. A comparison of the results of the forecast on the example confirms this conclusion.

KEYWORDS

time series, deterministic methods, comparative analysis of forecasting methods.

REFERENCES

  1. Box, G. and Jenkins, G. (1970), Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco, USA.
  2. Kalman, R.E. (1960), “A new approach to linear filtering and prediction problems”, Journal of Basic Engineering, Vol. 82, no. 1, pp. 35-45. 
    https://doi.org/10.1115/1.3662552
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  4. Rogoza, W.S. and Ischenko, G.V. (2022), “Method for forecasting short-term series using sensitivity functions”, Elektronne modelyuvannya, Vol. 44, no. 1, pp. 29-42.
    https://doi.org/10.15407/emodel.44.01.029

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