PDF Печать E-mail

DOI: https://doi.org/10.15407/techned2018.01.087


Journal Tekhnichna elektrodynamika
Publisher Institute of Electrodynamics National Academy of Science of Ukraine
ISSN 1607-7970 (print), 2218-1903 (online)
Issue No 1, 2018 (January/February)
Pages 87 – 93


P. Chernenko, O. Martyniuk, A. Zaslavsky, V. Miroshnyk
Institute of Electrodynamics National Academy of Sciences of Ukraine,
pr. Peremohy, 56, Kyiv, 03057, Ukraine,
e-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript



The issue of using a long-term prehistory to improve the accuracy of short-term forecasting the total electric load of the power system is considered. Information on hourly loads of regional energy systems and the main factors that affect them is stored in the developed database. The special control program can display information in graphically. Using the program for processing this information, a mathematical model of the electric load is constructed and changes in its characteristics over time are investigated under the influence of external factors. In order to test the effectiveness of the approach that involves the use of individual models trained in selected seasons on a sample of long-term pre-history, comparative prediction of hourly values of the total electric load for the day ahead was performed. Time intervals in the annual period that corresponds to the different characters of the effect of air temperature on the electric load are allocated. The proposed method, which consists in constructing independent mathematical models of electric load on the allocated time intervals with the use of long-term data. It improves the accuracy of modeling the influence of exogenous factors on the electric load of power system. The approach to modeling and forecasting of electric load of irregular days using the data of long-term prehistory is given. References 8, figures 3, tables 2.


Key words: power system, electrical load, mathematical modeling, short-term forecasting, long-term prehistory, exogenous factors.


Received:     19.07.2017
Accepted:     28.11.2017
Published:   29.01.2018



1. Kotsar О.V., Rasko Yu.О., Halabitskyi P. Increasing the reliability of forecasting the load of end-users in the BSMBM. Enerhetyka: ekonomika, tekhnolohii, ekolohiia. 2015. No 2(40). Pp. 43–52. (Ukr)
2. Makoklyiev B., Antonov A., Ushchapovskii K.V., Grabchak R.V. Forecasting the electric load of united power system of Ukraine. Elektricheskie seti i sistemy. 2010. No 4. Pp. 4–12. (Rus)
3. Chernenko P. Identification of parameters, modeling and multi-level interconnected forecasting of electrical loads of the united power system. Tekhnichna Elektrodynamika. Tematychnyi vypusk Problemy suchasnoi elektrotekhniky. 2010. Vol. 3. Pp. 57–64. (Rus)
4. Chernenko P.O., Martyniuk O.V., Zaslavsky A.I., Denesivich K.B. Improving the efficiency of planning united power system using the program complex of medium-term forecasting. Elektricheskie seti i sistemy. 2009. No 5. Pp. 21–35. (Rus)
5. Farmer E.D., Potton M.J. Development of on-line load prediction techniques with results from trials in the south-western region of the CFGB. Proc. IEE. 1968. Vol. 115. Pp. 1549–1558.
6. Galiana F., Handshin E., Fiechter A. Identification of stochastic electric load models from physical data. IEEE Trans. AC. 1974. Vol. 19. No 6. Pp. 887–893. https://doi.org/10.1109/TAC.1974.1100724
7. Gupta P.C., Yamada K. Adaptive short – term forecasting of hourly loads using weather information. IEEE Trans. Power Appar. and Syst. 1972. Vol. 91. No 5. Pp. 2085–2094. https://doi.org/10.1109/TPAS.1972.293541
8. Protasiewicz J. and Czczepaniak P.S. Neural Models of Demands for Electricity. Prediction and Risk Assessment. Electrical Review. 2012. Vol. 88. No 6. Pp. 272–279.