[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5370/JEET.2015.10.1.041

Prediction of Wind Power by Chaos and BP Artificial Neural Networks Approach Based on Genetic Algorithm

Huang, Dai-Zheng (school of Electrical Engineering, Guangxi University)
Gong, Ren-Xi (School of Electrical Engineering, Guangxi University)
Gong, Shu (School of Electrical Engineering, Guangxi University)

Publication Information

Journal of Electrical Engineering and Technology / v.10, no.1, 2015 , pp. 41-46 More about this Journal

Abstract

It is very important to make accurate forecast of wind power because of its indispensable requirement for power system stable operation. The research is to predict wind power by chaos and BP artificial neural networks (CBPANNs) method based on genetic algorithm, and to evaluate feasibility of the method of predicting wind power. A description of the method is performed. Firstly, a calculation of the largest Lyapunov exponent of the time series of wind power and a judgment of whether wind power has chaotic behavior are made. Secondly, phase space of the time series is reconstructed. Finally, the prediction model is constructed based on the best embedding dimension and best delay time to approximate the uncertain function by which the wind power is forecasted. And then an optimization of the weights and thresholds of the model is conducted by genetic algorithm (GA). And a simulation of the method and an evaluation of its effectiveness are performed. The results show that the proposed method has more accuracy than that of BP artificial neural networks (BP-ANNs).

Keywords

Wind power forecasting; Chaos and BP neural network method; Genetic algorithm;

Citations & Related Records

Times Cited By KSCI : 3 (Citation Analysis)

Reference
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