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

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

Victor Kurbatsky, Denis Sidorov, Nikita Tomin, Vadim Spiryaev
Copyright: © 2014 |Volume: 3 |Issue: 1 |Pages: 18
ISSN: 2160-9500|EISSN: 2160-9543|EISBN13: 9781466654075|DOI: 10.4018/ijeoe.2014010104
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MLA

Kurbatsky, Victor, et al. "Optimal Training of Artificial Neural Networks to Forecast Power System State Variables." IJEOE vol.3, no.1 2014: pp.65-82. http://doi.org/10.4018/ijeoe.2014010104

APA

Kurbatsky, V., Sidorov, D., Tomin, N., & Spiryaev, V. (2014). Optimal Training of Artificial Neural Networks to Forecast Power System State Variables. International Journal of Energy Optimization and Engineering (IJEOE), 3(1), 65-82. http://doi.org/10.4018/ijeoe.2014010104

Chicago

Kurbatsky, Victor, et al. "Optimal Training of Artificial Neural Networks to Forecast Power System State Variables," International Journal of Energy Optimization and Engineering (IJEOE) 3, no.1: 65-82. http://doi.org/10.4018/ijeoe.2014010104

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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.

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