A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation

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@Article{Shamshirband:2015:RSER,
  author =       "Shahaboddin Shamshirband and Kasra Mohammadi and 
                 Por Lip Yee and Dalibor Petkovic and Ali Mostafaeipour",
  title =        "A comparative evaluation for identifying the
                 suitability of extreme learning machine to predict
                 horizontal global solar radiation",
  journal =      "Renewable and Sustainable Energy Reviews",
  volume =       "52",
  pages =        "1031--1042",
  year =         "2015",
  ISSN =         "1364-0321",
  DOI =          "doi:10.1016/j.rser.2015.07.173",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1364032115008205",
  abstract =     "In this paper, the extreme learning machine (ELM) is
                 employed to predict horizontal global solar radiation
                 (HGSR). For this purpose, the capability of developed
                 ELM method is appraised statistically for prediction of
                 monthly mean daily HGSR using three different types of
                 input parameters: (1) sunshine duration-based (SDB),
                 (2) difference temperature-based (TB) and (3) multiple
                 parameters-based (MPB). The long-term measured data
                 sets collected for city of Shiraz situated in the Fars
                 province of Iran have been used as a case study. The
                 predicted HGSR via ELM is compared with those of
                 support vector machine (SVM), genetic programming (GP)
                 and artificial neural network (ANN) to ensure the
                 precision of ELM. It is found that higher accuracy can
                 be obtained by multiple parameters-based estimation of
                 HGSR using all techniques. The computational results
                 prove that ELM is highly accurate and reliable and
                 shows higher performance than SVM, GP and ANN. For
                 multiple parameters-based ELM model, the mean absolute
                 percentage error, mean absolute bias error, root mean
                 square error, relative root mean square error and
                 coefficient of determination are obtained as
                 2.2518percent, 0.4343 MJ/m2, 0.5882 MJ/m2,
                 2.9757percent and 0.9865, respectively. By conducting a
                 further verification, it is found that the ELM method
                 also offers high superiority over four empirical models
                 established for this study and an intelligent model
                 from the literature. In the final analysis, a proper
                 sensitivity analysis is performed to identify the
                 influence of considered input elements on HGSR
                 prediction in which the results reveal the significance
                 of appropriate selection of input parameters to boost
                 the accuracy of HGSR prediction by the ELM algorithm.
                 In a nutshell, the comparative results clearly specify
                 that ELM technique can provide reliable predictions
                 with further precision compared to the existing
                 techniques.",
  keywords =     "genetic algorithms, genetic programming, Horizontal
                 global solar radiation, Extreme learning machine (ELM),
                 Prediction, Comparative assessment, Sensitivity
                 analysis",
}

Genetic Programming entries for Shahaboddin Shamshirband Kasra Mohammadi Por Lip Yee Dalibor Petkovic Ali Mostafaeipour

Citations