A Wind Power Prediction Method Based on RBF Neural Network


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With the interconnection of the large-scale wind power, wind power forecasting is particularly important to the dispatcher of power grid. Based on the historical data, this paper proposes a prediction method based on RBF (radial basis function) neural network. This method is based on the model taking the influence of the system input (wind speed, wind direction, historical power output data) on the predicting error into consideration to get the optimal input values. Examples with field data obtained from Northwest of China show the effectiveness and higher precisionof the proposed method.



Edited by:

Fang Shao, Fenjie Long, Jie Liang, Haihong Chen and Meini Yuan




Y. R. Wang, "A Wind Power Prediction Method Based on RBF Neural Network", Applied Mechanics and Materials, Vols. 713-715, pp. 1107-1110, 2015

Online since:

January 2015





* - Corresponding Author

[1] Li Tao, Research on the Application of Support Vector Machine in Wind Power Prediction , Beijing Jiaotong University(2012).

[2] Feng Hongxia, Research on Wind Power Prediction and Wind Farm Energy Storage Capacity, Shan Dong University(2013).

[3] Wang Jiancheng, Study of short-term wind power prediction, South China University of Technology(2013).

[4] FAN Gaofeng, PEI Zheyi: submitted to Journal of Electric Power(2011).

[5] WANG Jian, YAN Gangui, SONG Wei and MU Gang: submitted to Journal Of Northeast Dianli University(2011).

[6] Fu Huixuan, Zhao Hong, MATLAB Neural Network Application Design (China Machine Press, Beijing 2010).

[7] Si Shoukui, SunXijing. Mathematic Modeling(National Defence Industry Press, Beijing2014).

[8] Song Wei, Large-scale Wind Farm Ultra-short Term Wind Power Prediction, Northeast Dianli University(2013).