ELECTRIC POWER CONSUMPTION FORECASTING BY METHODS OF NEURAL NETWORK MODELING
Keywords:
The capabilities of the Neural Network Toolbox software package for predicting power consumption are reviewed. Experimental research of work Neural Network Toolbox algorithms is conducted. The problems of providing data for study sample are found.Abstract
The capabilities of the Neural Network Toolbox software package for predicting power consumption are reviewed. Experimental research of work Neural Network Toolbox algorithms is conducted. The problems of providing data for study sample are found.
References
1. O.S. Haidenko. Metody prohnozuvannia elektrospozhyvannia tiahovymy pidstantsiiamy zaliznytsi [Methods of forecasting of electric power consumption by traction substations of the railway]. // Zb.nauk.prats «Modeliuvannia ta informatsiini tekhnolohii» [Collected Works «Modeling and Information Technologies»]. . – 2016. – issue 75. – Kiev, IPME im. G.E. Puhov NAS of Ukrainepp. – Р. 49-56. ISSN 2309-7647,
2. V.S. Medvedev. Neironnye sety. MATLAB 6 [Neural networks. MATLAB 6] / V.S. Medvedev, V.H. Potemkyn; Pod obshch. red. V.H. Potemkyna. – M.: DYALOH-MYFY, 2001. – 630 p. – (Pakety prikladnykh prohramm; Kn. 4 [Packages of application programs, Book 4]).
3. Fletcher R. Function minimization by conjugate gradients / Fletcher R., Reeves C.M. // Computer Journal. – Vol. 7. – 1964. – P. 149-154.
4. Hagan M.T. Training feedforward networks with the Marquardt algorithm / Hagan M.T., Menhaj M. // IEEE Transactions on Neural Networks. – Vol 5. – № 6. – 1994. – P. 989–993.
5. Osovskyj S. Neironnie seti dlia obrabotki informatsii [Neural networks for information processing] // Trans. from the Polish I.D. Rudinsky. – M: Finansy i statistika [Finance and Statistics], 2002. – 344 p.
Література:
1. Гайденко О.С. Методи прогнозування електроспоживання тяговими підстанціями залізниці. // Зб.наук.праць «Моделювання та інформаційні технології». – 2016. – Вип. 75. – Київ, ІПМЕ ім. Г.Є. Пу-хова НАН України. – С. 49-56. ISSN 2309-7647
2. Медведев В.С. Нейронные сети. MATLAB 6 / Медведев В.С., Потемкин В.Г.; Под общ. ред. В.Г. Потемкина. – М.: ДИАЛОГ-МИФИ, 2001. – 630 с. – (Пакеты прикладных программ; Кн. 4)
3. Fletcher R. Function minimization by conjugate gradients / Fletcher R., Reeves C.M. // Computer Journal. – Vol. 7, – 1964. – P. 149-154.
4. Hagan M.T. Training feedforward networks with the Marquardt algorithm / Hagan M.T., Menhaj M. // IEEE Transactions on Neural Networks. –Vol 5. – №6, – 1994. – С. 989–993.
5. Осовский С. Нейронные сети для обработки информации // Пер. с польского И.Д. Рудинского. – М.: Финансы и статистика, 2002. – 344 с.
Downloads
Published
How to Cite
Issue
Section
License
Copyright: This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
