The Model of Wind Power Short-Term Prediction Based on Artificial Fish Swarm Algorithm of Support Vector Machine

Abstract

In order to improve the accuracy of wind power prediction and solve the parameter selection problem of support vector machine(SVM)model for the wind power prediction, the artificial fish swarm algorithm(AFSA) is proposed to look for the support vector machine’s optimal parameter of kernel function and the parameter of error penalty. The model of AFSA-SVW is established to predict the wind power with the numerical weather forecast(NWP) data after clustering analysis. Form the result of simulation experiment, it shows that the model of AFSA-SVW has a higher accuracy than the model of BP and the model of BP and the model of BP and the model of PSO-SVM in the short-term wind power prediction.