Genetic Programming with Rough Sets Theory for Modeling Short-term Load Forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Wen-chuan Wang and Chun-tian Cheng and Lin Qiu",
  title =        "Genetic Programming with Rough Sets Theory for
                 Modeling Short-term Load Forecasting",
  booktitle =    "Fourth International Conference on Natural
                 Computation, ICNC '08",
  year =         "2008",
  month =        oct,
  volume =       "6",
  pages =        "306--310",
  keywords =     "genetic algorithms, genetic programming, China,
                 GuiZhou power grid, electric power operation,
                 evolutional algorithm, load demand, rough sets theory,
                 short-term load forecasting, load forecasting, power
                 grids, rough set theory",
  DOI =          "doi:10.1109/ICNC.2008.141",
  abstract =     "The accurate and robust short-term load forecasting
                 (STLF) plays a significant role in electric power
                 operation. The accuracy of STLF is greatly related to
                 the selected the main relevant influential factors.
                 However, how to select appropriate influential factor
                 is a difficult task because of the randomness and
                 uncertainties of the load demand and its influential
                 factors. In this paper, a novel method of genetic
                 programming (GP) with rough sets (RS) theory is
                 developed to model STLF to improve the accuracy and
                 enhance the robustness of load forecasting results. RS
                 theory is employed to process large data and eliminate
                 redundant information in order to find relevant factors
                 to the short-term load, which are used as sample sets
                 to establish forecasting model by means of GP
                 evolutional algorithm. The presented model is applied
                 to forecast short-term load using the actual data from
                 GuiZhou power grid in China. The forecasted results are
                 compared with BP artificial neural Network with RS
                 theory, and it is shown that the presented forecasting
                 method is more accurate and efficient.",
  notes =        "Also known as \cite{4667850}",

Genetic Programming entries for Wen-Chuan Wang Chun-Tian Cheng Lin Qiu