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

doi:10.11985/2016.10.002

Abstract

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-SVM is established to predict the wind power with the numerical weather forecast (NWP) data after clustering analysis. From the result of simulation experiment, it shows that the model of AFSA-SVM has a higher accuracy than the model of BP and the model of PSO-SVM in the short-term wind power prediction.

Key words: Artificial fish swarm algorithm, support vector machine, clustering analysis, wind power prediction

CLC Number:

TM715

Lijie Wang,Hong Li,Shibin Fang. The Model of Wind Power Short-Term Prediction Based on Artificial Fish Swarm Algorithm of Support Vector Machine[J]. Journal of Electrical Engineering, 2016, 11(10): 7-12.

Figures/Tables 8

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预测模型	NMAE(%)	NRMSE(%)
BP神经网络模型	18.80	22.85
PSO-SVM模型	15.26	18.27
AFSA-SVM模型	11.34	15.73

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