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

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@InProceedings{Fan:2010:ICNC,
  author =       "Xinqiao Fan and Yongli Zhu",
  title =        "The application of Empirical Mode Decomposition and
                 Gene Expression Programming to short-term load
                 forecasting",
  booktitle =    "Sixth International Conference on Natural Computation
                 (ICNC 2010)",
  year =         "2010",
  month =        "10-12 " # aug,
  volume =       "8",
  pages =        "4331--4334",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, empirical mode decomposition,
                 intrinsic mode functions, short-term load forecasting,
                 wavelet transforms, genetic algorithms, load
                 forecasting, statistical analysis, wavelet transforms",
  DOI =          "doi:10.1109/ICNC.2010.5583605",
  abstract =     "A forecasting method of combining Empirical Mode
                 Decomposition(EMD) and Gene Expression Programming(GEP)
                 that's called EMD and 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 and GEP in short-term
                 load forecasting is better.",
  notes =        "also known as \cite{5583605}",
}

Genetic Programming entries for Xinqiao Fan Yongli Zhu

Citations