Optimal Training of Artificial Neural Networks to Forecast Power System State Variables

Victor Kurbatsky, Denis Sidorov, Nikita Tomin and Vadim Spiryaev
Additional contact information
Victor Kurbatsky: Energy Systems Institute, Siberian Branch of the Russian Academy of Sciences, Irkutsk, Russia
Denis Sidorov: Energy Systems Institute, Siberian Branch of the Russian Academy of Sciences, Irkutsk, Russia
Nikita Tomin: Energy Systems Institute, Siberian Branch of the Russian Academy of Sciences, Irkutsk, Russia
Vadim Spiryaev: Energy Systems Institute, Siberian Branch of the Russian Academy of Sciences, Irkutsk, Russia

International Journal of Energy Optimization and Engineering (IJEOE), 2014, vol. 3, issue 1, 65-82

Abstract: The problem of forecasting state variables of electric power system is studied. The paper suggests data-driven adaptive approach based on hybrid-genetic algorithm which combines the advantages of genetic algorithm and simulated annealing algorithm. The proposed method has two stages. At the first stage the input signal is decomposed into orthogonal basis functions based on the Hilbert-Huang transform. The genetic algorithm and simulated annealing algorithm are applied to optimal training of the artificial neural network and support vector machine at the second stage. The results of applying the developed approach for the short-term forecasts of active power flows in the electric networks are presented. The best efficiency of proposed approach is demonstrated on real retrospective data of active power flow forecast using the hybrid-genetic support vector machine algorithm.

Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/ijeoe.2014010104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jeoe00:v:3:y:2014:i:1:p:65-82

Access Statistics for this article

International Journal of Energy Optimization and Engineering (IJEOE) is currently edited by Jose Marmolejo-Saucedo

More articles in International Journal of Energy Optimization and Engineering (IJEOE) from IGI Global
Bibliographic data for series maintained by Journal Editor ().