The application of Empirical Mode Decomposition and Gene Expression Programming to short-term load forecasting

A forecasting method of combining Empirical Mode Decomposition(EMD) and Gene Expression Programming(GEP) that´s called EMD&GEP method here is suggested, which is applied to short-term load forecasting and higher forecasting precision is obtained. The load samples are handled in order to eliminate the pseudo-data, and the intrinsic mode functions(IMFs) and the residual trend of different frequency are obtained according to EMD. Then the corresponding load series of the same time but different days in the IMFs and the residual trend are chosen as the training samples, and by means of the flexible expressive capacity of GEP, the models of different time points in each IMF and the residual trend are evolved according to time-sharing. And the final forecasting result is obtained by reconstructing the models of each IMF and the residual trend. The method of EMD overcomes the shortcomings of wavelet transform that it´s difficult to select proper wavelet function, and the final result indicates that the IMFs can reflect the characteristics of the power load. After comparison with the results forecasted by means of Wavelet and GEP, it proves that the effect of the forecasting method of EMD&GEP in short-term load forecasting is better.