Using measured daily meteorological parameters to predict daily solar radiation

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@Article{Mousavi:2015:Measurement,
  author =       "Seyyed Mohammad Mousavi and Elham S. Mostafavi and 
                 Alireza Jaafari and Arefeh Jaafari and 
                 Fariba Hosseinpour",
  title =        "Using measured daily meteorological parameters to
                 predict daily solar radiation",
  journal =      "Measurement",
  volume =       "76",
  pages =        "148--155",
  year =         "2015",
  ISSN =         "0263-2241",
  DOI =          "doi:10.1016/j.measurement.2015.08.004",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0263224115004017",
  abstract =     "A major factor for an efficient design of solar energy
                 systems is to provide accurate estimations of the solar
                 radiation. Many of the existing studies are focused on
                 the analysis of monthly or annual solar radiation. This
                 is while less attention has been paid to the
                 determination of daily solar radiation. Accordingly,
                 the main goal of this paper is to develop a robust
                 machine learning approach, based on genetic programming
                 (GP), for the estimation of the daily solar radiation.
                 The solar radiation is formulated in terms of daily air
                 temperature, relative humidity, atmospheric pressure,
                 wind speed, and earth temperature. A comprehensive
                 database containing about 7000 records collected for
                 about 20 years (1995-2014) in a nominal city in Iran is
                 used to develop the GP model. The performance of the
                 derived model is verified using different criteria. A
                 multiple linear regression analysis is performed to
                 benchmark the GP model with a classical technique. The
                 influences of the input variables on the solar energy
                 are evaluated through a sensitivity analysis. The
                 proposed model has a very good prediction performance
                 and significantly outperforms the traditional
                 regression model.",
  keywords =     "genetic algorithms, genetic programming, Daily solar
                 radiation, Machine learning, Sensitivity analysis,
                 Prediction",
}

Genetic Programming entries for Seyyed Mohammad Mousavi Elham S Mostafavi Alireza Jaafari Arefeh Jaafari Fariba Hosseinpour

Citations