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",
• month = jul,
• keywords = "genetic algorithms, genetic programming, Artificial intelligence, Artificial neural network, Distributed computing system, Hybrid models",
• ISSN = "0038-092X",
• URL = "http://www.sciencedirect.com/science/article/pii/S0038092X14000991",
• DOI = "doi:10.1016/j.solener.2014.02.021",
• size = "10 pages",
• 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

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