| 基于主成分分析的径向基函数神经网络在电力系统负荷预测中的应用 |
| |
| 引用本文： | 赵杰辉,葛少云,刘自发.基于主成分分析的径向基函数神经网络在电力系统负荷预测中的应用[J].电网技术,2004,28(5):35-37,40. |
| |
| 作者姓名： | 赵杰辉 葛少云 刘自发 |
| |
| 作者单位： | 天津大学电气与自动化工程学院,天津,300072 |
| |
| 摘 要： | 径向基函数(RBF)神经网络应用于电力系统负荷预测时,如果输入空间严重自相关及网络维数较高时,RBF神经网络的预测精度会下降.针对这一问题,文中提出了一种应用于电力负荷预测的改进RBF神经网络新方法.具体是利用主成分分析(PCA)方法对原输入空间进行重构,并根据各主成分的贡献率来确定网络结构,从而有效地解决了预测精度下降的问题.最后通过某省的实际算例验证了该方法的有效性.
|
| 关 键 词： | 主成分分析 径向基函数 人工神经网络 负荷预测 电力系统 |
| 文章编号： | 1000-3673(2004)05-0035-03 |
APPLICATION OF RADIAL BASIC FUNCTION NETWORK BASED ON PRINCIPAL COMPONENT ANALYSIS IN LOAD FORECASTING |
| |
| ZHAO Jie-hui,GE Shao-yun,LIU Zi-fa.APPLICATION OF RADIAL BASIC FUNCTION NETWORK BASED ON PRINCIPAL COMPONENT ANALYSIS IN LOAD FORECASTING[J].Power System Technology,2004,28(5):35-37,40. |
| |
| Authors: | ZHAO Jie-hui GE Shao-yun LIU Zi-fa |
| |
| Abstract: | When radial basic function (RBF) is applied to power load forecasting, if the input space is heavily self- correlated and the input numbers are too many, in that case too much centres of the neurons will be overlapped, finally the accuracy of load forecasting by RBF network will be descendent. To solve this problem the original input space is reconstructed by principal component analysis and the structure of the network is determined according to the contributions from the principal components respectively, thus, the above mentioned problem is effectively solved. The effectiveness of the proposed algorithm is verified by the practical data of a certain provincial power network. |
| |
| Keywords: | Principal component analysis Radial basic function (RBF) Artificial neural network (ANN) Load forecasting Power system |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
|
