Transport energy demand forecast using multi-level genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Mehdi Forouzanfar and A. Doustmohammadi and 
                 Samira Hasanzadeh and H. {Shakouri G}",
  title =        "Transport energy demand forecast using multi-level
                 genetic programming",
  journal =      "Applied Energy",
  volume =       "91",
  number =       "1",
  pages =        "496--503",
  year =         "2012",
  ISSN =         "0306-2619",
  DOI =          "doi:10.1016/j.apenergy.2011.08.018",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Transport
                 energy demand, Forecasting, Modelling",
  abstract =     "In this paper, a new multi-level genetic programming
                 (MLGP) approach is introduced for forecasting transport
                 energy demand (TED) in Iran. It is shown that the
                 result obtained here has smaller error compared with
                 the result obtained using neural network or fuzzy
                 linear regression approach. The forecast uses
                 historical energy data from 1968 to 2002 and it is
                 based on three parameters; gross domestic product
                 (GDP), population (POP), and the number of vehicles
                 (VEH). The approach taken in this paper is based on
                 genetic programming (GP) and the multi-level part of
                 the name comes from the fact that we use GP in two
                 different levels. At the first level, GP is used to
                 obtain the time series model of the three parameters,
                 GDP, POP, and VEH, and forecast those parameters for
                 the time interval that their actual data are not
                 available, and at the second level GP is used one more
                 time to forecast TED based on available data for TED
                 along with the data that are either available or
                 predicted for the three parameters discussed earlier.
                 Actual data from 1968 to 2002 are used for training and
                 the data for years 2003-2005 are used to test the GP
                 model. We have limited ourselves to these data ranges
                 so that we could compare our results with the existing
                 ones in the literature. The estimation GP for the model
                 is formulated as a nonlinear optimisation problem and
                 it is solved numerically.",

Genetic Programming entries for Mehdi Forouzanfar Ali Doustmohammadi Samira Hasanzadeh G Hamed Shakouri