A linear genetic programming approach for the prediction of solar global radiation

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{journals/nca/ShavandiR13,

• author = "Hassan Shavandi and Sara Saeidi Ramiyani",
• title = "A linear genetic programming approach for the prediction of solar global radiation",
• journal = "Neural Computing and Applications",
• year = "2013",
• volume = "23",
• number = "3-4",
• pages = "1197--1204",
• month = sep,
• keywords = "genetic algorithms, genetic programming, solar global radiation, linear genetic programming, climatological parameters, prediction",
• publisher = "Springer",
• ISSN = "0941-0643",
• bibdate = "2013-09-24",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca23.html#ShavandiR13",
• URL = "http://dx.doi.org/10.1007/s00521-012-1039-6",
• DOI = "doi:10.1007/s00521-012-1039-6",
• abstract = "in this article, the linear genetic programming (LGP) is used to predict the solar global radiation. The solar radiation is formulated in terms of several climatological and meteorological parameters. Comprehensive databases containing monthly data collected for 6 years (1995-2000) in two nominal cities in Iran are used to develop LGP-based models. Separate models are established for each city. To verify the performance of the proposed models, they are applied to estimate the solar global radiation of test data of database. The contribution of the parameters affecting the solar radiation is evaluated through a sensitivity analysis. The results indicate that the LGP models give precise estimations of the solar global radiation and significantly outperform traditional Angstrom's model.",
• notes = "second author name corrected as per erratum",

}

Genetic Programming entries for Hassan Shavandi Sara Saeidi Ramiyani

Citations