МОДЕЛЮВАННЯ ТА КОРОТКОСТРОКОВЕ ПРОГНОЗУВАННЯ ТЕХНОЛОГІЧНОЇ СКЛАДОВОЇ ЕЛЕКТРИЧНОГО НАВАНТАЖЕННЯ ОБЛАСНОЇ ЕНЕРГОСИСТЕМИ

P.A. Chernenko; O.V. Martyniuk; V.O. Miroshnyk

doi:10.15407/techned2016.04.068

No. 4 (2016), Electric Power Systems and Installations

No. 4 (2016)

Electric Power Systems and Installations

MODELING AND SHORT-TERM FORECASTING OF TECHNOLOGY COMPONENT OF ELECTRICAL LOAD OF THE REGIONAL ELECTRIC POWER SYSTEM

Published 2016-06-21

https://doi.org/10.15407/techned2016.04.068

P.A. Chernenko⁺⁻
O.V. Martyniuk⁺⁻
V.O. Miroshnyk⁺⁻

P.A. Chernenko

Institute of Electrodynamics of the National Academy of Sciences of Ukraine, pr. Peremohy, 56, Kyiv, 03057, Ukraine

O.V. Martyniuk

Institute of Electrodynamics of the National Academy of Sciences of Ukraine, pr. Peremohy, 56, Kyiv, 03057, Ukraine

V.O. Miroshnyk

Institute of Electrodynamics of the National Academy of Sciences of Ukraine, pr. Peremohy, 56, Kyiv, 03057, Ukraine

ARTICLE_22_PDF (Українська)

Keywords

electric power system
electrical load
mathematical model
short-term forecasting
energy-intensive enterprises
artificial neural network енергосистема
електричне навантаження
математична модель
короткострокове прогнозування
енергоємні підприємства
штучна нейронна мережа модель Бокса-Дженкінса

How to Cite

[1]

Chernenko, P. et al. 2016. MODELING AND SHORT-TERM FORECASTING OF TECHNOLOGY COMPONENT OF ELECTRICAL LOAD OF THE REGIONAL ELECTRIC POWER SYSTEM. Tekhnichna Elektrodynamika. 4 (Jun. 2016), 068. DOI:https://doi.org/10.15407/techned2016.04.068.

Abstract

The proposed method for a total electrical load of the regional electric power system forecasting is described. To model a technology load component, artificial intelligence techniques and autoregressive Box-Jenkins models are used. The advantages and disadvantages of different forecast models are analyze. To solve the mentioned task, an optimal type, architecture and vector of model input parameters are determined. Approbation was conducted on actual data taken from the regional electric power system with advantage of industrial power consumption. References 4, table 1.

https://doi.org/10.15407/techned2016.04.068

ARTICLE_22_PDF (Українська)

References

Bodyanskiy Ye., Popov S., Rybalchenko T. Feedforward neural network with a specialized architecture for estimation of the temperature influence on the electric load // Proc. 4th International IEEE Conference "Intelligent Systems". – Varna, 2008. – Vol. I. – Pp. 714–718.

Box G., Jenkins G. Time Series Analysis: Forecasting and Control. – Мoskva: Мir, 1974.

Chernenko P., Martyniuk O., Miroshnyk V., Zaslavsky A. Two-stage verification of daily schedules electrical loads of power system with the significant part of industrial power consumption//Enerhetyka ta Elektryfikatsiia. – 2015. – No 7. – Pp. 10– 23. (Ukr)

Hippert H. S., Pedreira C. E., Souza R. C. Neural networks for short-term load forecasting: a review and evaluation // IEEE Trans. Power Systems. – 2001. – Vol. 16. – No 1. – Pp. 44-55.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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MODELING AND SHORT-TERM FORECASTING OF TECHNOLOGY COMPONENT OF ELECTRICAL LOAD OF THE REGIONAL ELECTRIC POWER SYSTEM

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