ASSESSMENT OF INFLUENCING FACTORS AND FORECASTING OF POWER CONSUMPTION IN THE REGIONAL POWER SYSTEM, TAKING INTO ACCOUNT ITS OPERATING MODE
Abstract
The article is devoted to the research of the assessment of influencing factors and forecasting
of power consumption in the regional power system, taking into account its operating modes.
The analysis of existing methods of forecasting energy consumption is carried out. The choice of a
forecasting method using an artificial neural network is justified. An algorithm for creating a neural
network for short-term prediction of electrical load is considered. The relevance of the work is
due to the requirements of the current legislation for forecasting electricity consumption in order
to solve the problem of maintaining a balance of power between the generating side and the consumption
of electric energy. At the same time, one of the main tasks related to the generation of
electric energy and its consumption is the task of maintaining a balance of capacities. On the one
hand, with an increase in the planned load, interruptions in the supply of electricity may occur, on
the other hand, a decrease in electricity consumption will also lead to a decrease in the efficiency
of power plants, and ultimately to an increase in the cost of electricity both for the wholesale electricity
market and for the end user. The developed neural network model reduces the task of shortterm
forecasting of power consumption to the search for a matrix of free coefficients by training
on available statistical data (active and re-active power, ambient temperature, date and index of
the day). The received NS model of short-term forecasting of power consumption of a section of
the district 10 kV electric grid takes into account the factors: – time, - meteorological conditions,
– disconnections of individual power supply lines of cottages, – operating mode of electricity consumers.
Predictive estimates of the power consumption of the power system have been obtained
based on the data of the electricity consumed by the outdoor temperature, the type of day, etc. The
model for predicting the magnitude of the consumed active and reactive power is quite workable,
but at this stage still has a fairly high level of forecasting error. To improve the accuracy of forecasting,
it is necessary to increase the database that makes up the training sample, because at the
moment the available data cover a time period of only 3–4 months. The results of the analysis
showed that forecasting reactive power consumption causes the greatest difficulties.
References
industry: Federal Law of the Russian Federation No. 35-F3 of 26.03.2003], Rossiyskaya
Gazeta [Russian newspaper], 1 Apr. 2003, pp. 1.
2. Rudakov E., Saakyan Yu., Nigmatulin B., Prokhorova N. Tsena rastochitel'nosti [The price of
wastefulness], Ekspert [Expert], No. 24 / June iyunya 2008.
3. Serebryakov N.A., Khomutov S.O. Analiz sluchaynoy sostavlyayushchey vremennogo ryada
elektricheskoy nagruzki gruppy tochek postavki elektroenergii sel'khozproizvoditeley [Analysis
of the random component of the time series of the electric load of the group of points of
electricity supply of agricultural producers], Vestnik Altayskogo gosudarstvennogo agrarnogo
universiteta [Bulletin of the Altai State Agrarian University], 2019, No. 5 (175), pp. 153 -158.
4. Planirovanie elektroenergeticheskikh rezhimov raboty energoob"ektov i energosistem [Planning
of electric power modes of operation of power facilities and power systems], Sayt AO
«Sistemnyy operator Edinoy energeticheskoy sistemy» [Website of JSC "System Operator of
the Unified Energy System"]. Available at: http://so-ups.ru/index.php?id=regime_plan
(14.06.2016).
5. Dubyago M., Polyuyanovich N., Azarov N., Ogrenichev A. Development and research of the
forecasting methods of the cable lines throughput using artificial neural networks, Proceedings
– ICOECS 2020: 2020 International Conference on Electrotechnical Complexes and Systems,
2020, pp. 9278503.
6. Domanov V.I., Bilalova A.I. Prognozirovanie ob"emov energopotrebleniya v zavisimosti ot
iskhodnoy informatsii [Forecasting the volume of energy consumption depending on the initial
information], Vestnik YuUrGU. Seriya «Energetika» [Bulletin of SUSU. The series "Energy"],
2016, Vol. 16, No. 2. pp. 59-65.
7. Poluyanovich N.K., Azarov N.V., Ogrenichev A.V., Dubyago M.N. Mnogoetapnyy metod
kratkosrochnogo prognozirovaniya temperaturnykh rezhimov v silovom kabele [Multi-stage
method of short-term forecasting of temperature regimes in a power cable], Izvestiya YuFU.
Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2020, No. 2 (212), pp. 38-51.
8. Poluyanovich N.K., Dubyago M.N. Analiz i vybor metodiki v reshenii zadach
intellektualizatsii sistem prognozirovaniya termofluktuatsionnykh protsessov v kabel'nykh
setyakh [Analysis and choice of methodology in solving problems of intellectualization of
forecasting systems of thermal fluctuation processes in cable networks], Izvestiya YuFU.
Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2020, No. 2 (212), pp. 52-66.
9. Zheng Н., Yuan J., Chen L. Short-term load forecasting using EMD-LSTM neural networks
with a XGBOOSt algorithm for feature importance evaluation, Energies, 2017; 10:1-20. DOI:
10.3390/en10081168.
10. Sharifullin V.N., Sharifullina A.V. Neyrosetevoe prognozirovanie potrebleniya elektroenergii
promyshlennym predpriyatiem [Neural network forecasting of electricity consumption by an
industrial enterprise], Vestnik Kazanskogo gosudarstvennogo energeticheskogo universiteta
[Bulletin of the Kazan State Energy University], 2012, No. 2 (13), pp. 6-11.
11. Domanov V.I., Bilalova A.I. Analiz prognozirovaniya energopotrebleniya s razlichnymi
informatsionnymi bazami [Analysis of energy consumption forecasting with various information
bases], Izvestiya Samarskogo nauchnogo tsentra Rossiyskoy akademii nauk [Izvestiya Samara
Scientific Center of the Russian Academy of Sciences], 2014, Vol. 16, No. 4–3, pp. 535-537.
12. Kirpichnikova I.M., Saplin L.A., Solomakho K.L. Prognozirovanie ob"emov potrebleniya
elektroenergii [Forecasting the volume of electricity consumption], Vestnik YUzhno-
Ural'skogo gosudarstvennogo universiteta. Seriya: Energetika [Bulletin of the South Ural
State University. Series: Energy], 2014, Vol. 14, No. 2, pp. 16-22,
13. Varfolomeeva A.A. Lokal'nye metody prognozirovaniya s vyborom metriki [Local forecasting
methods with a choice of metrics], Mashinnoe obuchenie i analiz dannykh [Machine learning
and data analysis], 2012, Vol. 1, No. 3, pp. 367-375.
14. Sunagatov I.M. Analiz i modifikatsiya metoda prognozirovaniya energopotrebleniya s
pomoshch'yu ekstrapolyatsii vyborki maksimal'nogo podobiya [Analysis and modification of
the method of forecasting energy consumption using the extrapolation of the maximum similarity
sample], Vestnik Samarskogo gosudarstvennogo tekhnicheskogo universiteta. Seriya:
Tekhnicheskie nauki [Bulletin of the Samara State Technical University. Series: Technical Sciences],
2013, No. 1 (37), pp. 223-226.
15. Khaykin S. Neyronnye seti: polnyy kurs [Neural networks: a complete course]. 2nd. ed.: transl.
from engl. Moscow: Izdatel'skiy dom «Vil'yams», 2006, 1104 p.
16. Poluyanovich N.K., Dubyago M.N. Prognozirovanie resursa kabel'nykh liniy s ispol'zovaniem
metoda iskusstvennykh neyronnykh setey [Forecasting the resource of cable lines using the
method of artificial neural networks], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU.
Engineering Sciences], 2019, No. 3 (205), pp. 51-62.
17. Poluyanovich N.K., Dubyago M.N., Azarov N.V., Ogrenichev A.V. Neyrosetevoy metod v
zada-chakh prognozirovaniya elektropotrebleniya v elektroenergeticheskoy sisteme [Neural
network method in the tasks of forecasting electricity consumption in the electric power system],
Matematicheskie metody v tekhnologiyakh i tekhnike [Mathematical methods in technology
and engineering], 2022, No. 1, pp. 114-118.
18. Kulbarakov M.A. K zadache prognozirovaniya energopotrebleniya s pomoshch'yu neyronnykh
setey [On the problem of forecasting energy consumption using neural networks], Molodoy
uchenyy [Young scientist], 2014, No. 11 (70), pp. 22-25. Available at:
https://moluch.ru/archive/70/12122/ (accessed: 05 December 2021).
19. Poluyanovich N.K., Burkov D.V., Dubyago M.N., Shurykin A.A., Kosenko E.Y. The influence
of the electromagnetic field on the neural network monitoring of insulation materials for electric
cable networks, International Conference on Electrical, Computer, Communications and
Mechatronics Engineering, ICECCME 2021, 2021, pp. 9590839.
20. Pshikhopov V.Kh., Gayduk A.R., Medvedev M.Yu., Belyaev V.E., Poluyanovich N.K., Voloshchenko
Yu.P. Energosberegayushchee upravlenie tyagovymi privodami elektropodvizhnogo sostava [Energy-
saving control of traction drives of electric rolling stock], Izvestiya YuFU. Tekhnicheskie nauki
[Izvestiya SFedU. Engineering Sciences], 2013, No. 2 (139), pp. 192-200.
21. Poluyanovich N.K., Tibeyko I.A. Ekspluatatsiya i remont sistem elektrosnabzheniya
promyshlennykh predpriyatiy [Operation and repair of power supply systems of industrial enterprises].
Taganrog, 2014.
22. Dubyago M.N., Poluyanovich N.K. Sovershenstvovanie metodov diagnostiki i
prognozirovaniya elektroizolyatsionnykh materialov sistem energosnabzheniya: monografiya
[Improvement of methods of diagnostics and forecasting of electrical insulation materials of
power supply systems: monograph]. Rostov-on-Don; Taganrog: Izd-vo YuFU, 2019, 192 p.
