Prediction of Wind Power Generation Through Combining Particle Swarm Optimization and Elman Neural Network (El-PSO)

Azim Heydari, Farshid Keynia


In recent years, rapid advances in wind energy production in many countries have made the prediction of wind power very important. In addition, wind power is a complicated signal for modeling and prediction. According to previous studies in this field, wind power prediction requires an efficient method. In the current survey, a method which is a combination of two intelligent methods of Elman neural network and Particle Swarm Algorithm is proposed to predict wind power. The efficiency of the proposed prediction method is shown for predicting of wind power output of wind farms. Results of El-PSO suggested method and El-GA method were compared and evaluated by analysis of variance method (ANOVA). All the results indicate efficient performance of the proposed method (El-PSO).


ANOVA, El-GA method, El-PSO method, Elman neural network and wind power

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