Wind Power Forecast by Using Improved Radial Basis Function Neural Network

被引：0

作者：

Lu, H. J. ^{[1
]}

Chang, G. W. ^{[1
]}

机构：

[1] Natl Chung Cheng Univ, Dept Elect Engn, Chiayi, Taiwan

来源：

2018 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM) | 2018年

关键词：

Wind power forecast; back propagation neural network; radial basis function neural network; SPEED; COMBINATION;

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：

Forecasting wind speed or wind power generation is indispensable for the effective operation of a wind farm, and the optimal management of its revenue and risks. This paper proposes an improved radial basis function neural network structure for forecasting the wind power generation. Results are then compared with back propagation neural network (BPNN), BPNN with Levenberg-Marquardt (BPNN-LM), radial basis function neural network (RBFNN), and the actual measured wind power outputs. Test results show that the presented model can provide more accurate and stable time-horizons forecasting.

引用

页数：5

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