基于经验模态分解和基因表达式程序设计的电力系统短期负荷预测

提出将经验模态分解（EMD）和基因表达式程序设计（GEP）算法相结合的EMD&GEP预测法应用于电力系统短期负荷预测中，消除负荷样本中的伪数据，并对负荷样本序列进行经验模态分解得到不同频段的本征模态分量（IMF）和负荷剩余分量。运用基因表达式程序设计算法的灵活表达能力，把分解得到的不同频段的各负荷本征模态分量及负荷剩余分量中所对应的不同日、同一时刻的负荷序列作为样本，进行分时预测。把各负荷本征模态分量和负荷剩余分量中相对应的预测结果进行重构，作为各时刻负荷的最终预测值。EMD克服了小波分析中小波基选取困难的不足，结果表明各负荷本征模态分量能较准确反映负荷特征，而且经比较，EMD&GEP预测法比小波分析和GEP算法相结合的预测方法具有更好的预测效果。

Short-term load forecasting based on empirical mode decomposition and gene expression programming

A forecasting method based on Empirical Mode Decomposition(EMD) and Gene Expression Programming(GEP) that's called EMD&GEP here is presented and applied to short-term load forecasting.Firstly,the load samples are handled in order to eliminate the pseudo-data,and the intrinsic mode functions(IMFs) and the residue of different frequency bands are obtained according to EMD .Then the corresponding load series of the same time butdifferent days in the IMFs and the residue are chosen as the training samples. By m...