Forecasting energy consumption using a grey model improved by incorporating genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Yi-Shian Lee and Lee-Ing Tong",
  title =        "Forecasting energy consumption using a grey model
                 improved by incorporating genetic programming",
  journal =      "Energy Conversion and Management",
  volume =       "52",
  number =       "1",
  pages =        "147--152",
  year =         "2011",
  ISSN =         "0196-8904",
  DOI =          "doi:10.1016/j.enconman.2010.06.053",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Energy
                 consumption, Grey forecasting model",
  abstract =     "Energy consumption is an important economic index,
                 which reflects the industrial development of a city or
                 a country. Forecasting energy consumption by
                 conventional statistical methods usually requires the
                 making of assumptions such as the normal distribution
                 of energy consumption data or on a large sample size.
                 However, the data collected on energy consumption are
                 often very few or non-normal. Since a grey forecasting
                 model, based on grey theory, can be constructed for at
                 least four data points or ambiguity data, it can be
                 adopted to forecast energy consumption. In some cases,
                 however, a grey forecasting model may yield large
                 forecasting errors. To minimise such errors, this study
                 develops an improved grey forecasting model, which
                 combines residual modification with genetic programming
                 sign estimation. Finally, a real case of Chinese energy
                 consumption is considered to demonstrate the
                 effectiveness of the proposed forecasting model.",

Genetic Programming entries for Yi-Shian Lee Lee-Ing Tong