Forecasting nonlinear time series of energy consumption using a hybrid dynamic model

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@Article{Lee2012251,
  author =       "Yi-Shian Lee and Lee-Ing Tong",
  title =        "Forecasting nonlinear time series of energy
                 consumption using a hybrid dynamic model",
  journal =      "Applied Energy",
  volume =       "94",
  pages =        "251--256",
  year =         "2012",
  ISSN =         "0306-2619",
  DOI =          "doi:10.1016/j.apenergy.2012.01.063",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0306261912000694",
  keywords =     "genetic algorithms, genetic programming, Energy
                 consumption, Grey forecasting model, Hybrid dynamic
                 approach",
  abstract =     "Energy consumption is an important index of the
                 economic development of a country. Rapid changes in
                 industry and the economy strongly affect energy
                 consumption. Although traditional statistical
                 approaches yield accurate forecasts of energy
                 consumption, they may suffer from several limitations
                 such as the need for large data sets and the assumption
                 of a linear formula. This work describes a novel hybrid
                 dynamic approach that combines a dynamic grey model
                 with genetic programming to forecast energy
                 consumption. This proposed approach is used to forecast
                 energy consumption because of its excellent accuracy,
                 applicability to cases with limited data sets and ease
                 of computability using mathematical software. Two case
                 studies of energy consumption demonstrate the
                 reliability of the proposed model. Computational
                 results indicate that the proposed approach outperforms
                 other models in forecasting energy consumption.",
}

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

Citations