Forecasting Wind Power Generation by A New Type of Radial Basis Function-based Neural Network

被引：0
|
作者：
Chang, G. W. [1 ]
Lu, H. J. [1 ]
Chen, Y. Y. [1 ]
Chang, Y. R. [2 ]
机构：
[1] Natl Chung Cheng Univ, Dept Elect Engn, Chiayi, Taiwan
[2] Atom Energy Council, Inst Nucl Energy Res, Taoyuan, Taiwan
来源：
2017 IEEE POWER & ENERGY SOCIETY GENERAL MEETING | 2017年
关键词：
Wind power forecast; radial basis function neural network; Gaussian mixture model; SPEED; PREDICTION;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
The importance of short-term wind power forecasting is significantly increased because of the demand of green energy and large-scale integration of the wind power plants in the electric network. In this paper, a Gaussian mixture model (GMM)-based radial basis function neural network is proposed to forecast the short-term wind power generation. Actual measured wind power output data are adopted to implement the proposed model. Test results of wind power obtained by autoregressive integrated moving average (ARIMA), back propagation neural network (BPNN), radial basis function neural network (RBFNN), support vector regression (SVR), and the proposed method are then under comparisons. Simulated results show that the presented method leads to more accurate wind power forecasting.
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页数：5
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