电力系统保护与控制Issue(15)：80-86,7.
基于改进的小波-BP神经网络的风速和风电功率预测
Wind speed and power prediction based on improved wavelet-BP neural network
摘要
Abstract
In order to improve the forecasting accuracy of ultra-short-term wind power, the improved wavelet-BP neural network method is applied. To solve the widespread delay problems of the prediction model, the original signal is decomposed into high and low frequency signal by the discrete wavelet transform. Moreover, genetic algorithm is used to optimize the BP neural network model separately. Finally, the summation of all the prediction results is gotten. As the wind speed and power series have chaos characteristics, the C-C method is used to optimize parameters of phase space reconstruction and the embedded dimension is taken as the input layer’s node number of neural network. It is applied in a wind farm, in Shandong Province, and the simulation results show that it has higher prediction accuracy than BP neural network model in forecasting wind speed and power. With the conversion of wind speed prediction results by the measured power curve, the power prediction accuracy goes bad.关键词
小波分析/相空间重构/C-C法/遗传算法/神经网络/功率曲线转换法Key words
wavelet analysis/phase-space reconstruction/C-C method/genetic algorithm/neural network/power curve conversion method分类
信息技术与安全科学引用本文复制引用
肖迁,李文华,李志刚,刘金龙,刘会巧..基于改进的小波-BP神经网络的风速和风电功率预测[J].电力系统保护与控制,2014,(15):80-86,7.基金项目
国家自然科学基金项目（51377044）This work is supported by National Natural Science Foundation of China (No.51377044) （No.51377044）
