期刊论文、会议论文

中文

电气技术

1673-3800

2014

6

44-47

2014第九届中国电工装备创新与发展论坛

2014第九届中国电工装备创新与发展论坛论文集

2014-06-21

北京

中国电工技术学会

10.3969/j.issn.1673-3800.2014.06.011

本校为第一机构

针对风速非平稳、非线性的特点,为提高短期风速预测准确性与快速性,建立了EMD-RBFNN预测模型。利用经验模态分解(EMD)将风速时间序列分解为具有相同特征尺度的相对平稳的本征模态(IMF)分量,以实现风速时间序列信号平稳化;针对各个模态分量的特性,采用径向基函数神经网络模型(RBFNN)对各个模态分量分别进行预测,选用正交最小二乘法来最大限度减少错误率,最后将各IMF-RBFNN预测结果进行重构得到最终预测值;设计并实现了基于GUI交互式界面的多功能短期风速预测系统。实验结果表明,EMD-RBFNN预测模型有效提高了短期风速预测精度,具有一定的实用价值。

Aiming at the nonlinear and nonstationary of wind speed sequences, a novel method based on empirical mode decomposition(EMD) and radial basis function neural network(RBFNN) is proposed to improve the precision of short-term wind speed forecasting. The wind speed data is decomposed into a series of intrinsic mode function(IMF) components with similar time-frequency characteristics and stationary by using EMD to achieve the stationary of the wind speed data. The IMF components are predicted by RBFNN based on the time-frequency characteristics of different IMF components. The orthogonal least squ...