Global solar irradiation prediction using a multi-gene genetic programming approach

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

@Article{oai:arXiv.org:1403.0623,
  author =       "Indranil Pan and Daya Shankar Pandey and 
                 Saptarshi Das",
  title =        "Global solar irradiation prediction using a multi-gene
                 genetic programming approach",
  journal =      "Journal of Renewable and Sustainable Energy",
  year =         "2013",
  volume =       "5",
  number =       "6",
  keywords =     "genetic algorithms, genetic programming, computer
                 science - neural and evolutionary computing, computer
                 science - computational engineering, finance, and
                 science, statistics - applications",
  eid =          "063129",
  bibsource =    "OAI-PMH server at export.arxiv.org",
  identifier =   "Journal of Renewable and Sustainable Energy, vol. 5,
                 no. 6, pp. 063129, 2013; doi:10.1063/1.4850495",
  oai =          "oai:arXiv.org:1403.0623",
  URL =          "http://arxiv.org/abs/1403.0623",
  URL =          "http://scitation.aip.org/content/aip/journal/jrse/5/6/10.1063/1.4850495",
  DOI =          "doi:10.1063/1.4850495",
  abstract =     "In this paper, a nonlinear symbolic regression
                 technique using an evolutionary algorithm known as
                 multi-gene genetic programming (MGGP) is applied for a
                 data-driven modelling between the dependent and the
                 independent variables. The technique is applied for
                 modelling the measured global solar irradiation and
                 validated through numerical simulations. The proposed
                 modelling technique shows improved results over the
                 fuzzy logic and artificial neural network (ANN) based
                 approaches as attempted by contemporary researchers.
                 The method proposed here results in nonlinear
                 analytical expressions, unlike those with neural
                 networks which is essentially a black box modelling
                 approach. This additional flexibility is an advantage
                 from the modelling perspective and helps to discern the
                 important variables which affect the prediction. Due to
                 the evolutionary nature of the algorithm, it is able to
                 get out of local minima and converge to a global
                 optimum unlike the back-propagation (BP) algorithm used
                 for training neural networks. This results in a better
                 percentage fit than the ones obtained using neural
                 networks by contemporary researchers. Also a hold-out
                 cross validation is done on the obtained genetic
                 programming (GP) results which show that the results
                 generalise well to new data and do not over-fit the
                 training samples. The multi-gene GP results are
                 compared with those, obtained using its single-gene
                 version and also the same with four classical
                 regression models in order to show the effectiveness of
                 the adopted approach.",
}

Genetic Programming entries for Indranil Pan Daya Shankar Pandey Saptarshi Das

Citations