Genetic programming for photovoltaic plant output forecasting

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@Article{Russo:2014:SE,
  author =       "M. Russo and G. Leotta and P. M. Pugliatti and 
                 G. Gigliucci",
  title =        "Genetic programming for photovoltaic plant output
                 forecasting",
  journal =      "Solar Energy",
  volume =       "105",
  pages =        "264--273",
  year =         "2014",
  ISSN =         "0038-092X",
  DOI =          "doi:10.1016/j.solener.2014.02.021",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0038092X14000991",
  keywords =     "genetic algorithms, genetic programming, Artificial
                 intelligence, Artificial neural network, Distributed
                 computing system, Hybrid models",
  abstract =     "In this paper we have identified several mathematical
                 models for predicting the solar power output of a 1.05
                 kWp Monocrystalline Silicon high-efficiency
                 photovoltaic string located at the ENEL Catania site,
                 Italy. The data we used corresponds to 15 min of
                 averaged power generated over a whole year (2010). A
                 tool named the Brain Project was used. It follows a
                 distributed genetic programming approach. Seventy-four
                 inputs were investigated for our purposes, but no cloud
                 information was considered. The accuracy of all the
                 models was evaluated and compared to other approaches.
                 Among these, the simpler models, that foresee only two
                 inputs perform similarly to our more complex models and
                 to several others in literature.",
}

Genetic Programming entries for Marco Russo G Leotta P M Pugliatti G Gigliucci

Citations