神经网络短期负荷预测中的数据分析

Data Analysis Method for Neural Network Based on Short-term Load Forecasting

在运用神经网络进行短期电力负荷预测中 ,输入数据的复杂性和冗余性给网络训练的效率和预测精度造成了显著的负面影响。文中提出了一种复合的数据分析方法 ,先采用输入变量贡献分析方法 ,根据输入变量对输出贡献的大小划分为主要变量和次要变量 ,在保留主要变量的基础上 ,再采用多元统计分析中的主成分分析法 ,消除变量间的线性相关性 ,以此达到压缩变量维数的目的。将此分析方法用于处理神经网络的输入变量 ,提取其主要成分 ,使结构大为简化。结果表明 ,经该方法处理后的数据输入神经网络 ,训练时间大幅度缩短 。

In neural network based short-term load forecasting, complexity and redundancy of input data have a negative effect on network training efficiency and forecasting precision. Focusing on solving this problem, a multiple method of data processing is developed. Firstly a method called input variable contribution analysis is applied, which divides input variables into primary variables and minor variables according to their contribution to network output. Minor variables are tossed out. Then principal component analysis is applied to primary variables to eliminate linear correlation among them, thus reduce the variable dimension. Based on this method, the main components are gotten, and then simplified network structure is designed. The result shows that after data processing, the training time is reduced noticeably and forecasting precision is enhanced.