Short-Term Load Forecasting Based on the Method of Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Huo:2007:ICMA,
  author =       "Limin Huo and Xinqiao Fan and Yunfang Xie and 
                 Jinliang Yin",
  title =        "Short-Term Load Forecasting Based on the Method of
                 Genetic Programming",
  booktitle =    "International Conference on Mechatronics and
                 Automation, ICMA 2007",
  year =         "2007",
  pages =        "839--843",
  address =      "Harbin, China",
  month =        "5-8 " # aug,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-0828-3",
  DOI =          "doi:10.1109/ICMA.2007.4303654",
  abstract =     "The algorithm of Genetic Programming is described and
                 applied to short-term load forecasting. For the fault
                 in history load data, the load samples are filtered and
                 processed generally before using, and then the load
                 series of the same time point but different days are
                 chosen as the training sets. According to the complex
                 expressive capacity of Genetic Programming, the future
                 short-term load model of different time point is
                 forecasted by time-sharing. This method of Genetic
                 Programming can find out relevant elements to electric
                 load data automatically, so the artificial errors in
                 forecasting can be avoided effectively. And the future
                 load value of each time point can be calculated with
                 the corresponding model created. Finally, it proves
                 that the method of Genetic Programming in short-term
                 load forecasting is better through out comparison
                 between the results forecasted by Genetic Programming
                 and time series.",
  notes =        "Department of Mechanical and Electronic Engineering,
                 Agricultural University of Hebei, Baoding 071001,
                 China.",
}

Genetic Programming entries for Yuan Hongbo Xinqiao Fan Yunfang Xie Jinliang Yin

Citations