A support vector machine-firefly algorithm-based model for global solar radiation prediction

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@Article{Olatomiwa:2015:SE,
  author =       "Lanre Olatomiwa and Saad Mekhilef and 
                 Shahaboddin Shamshirband and Kasra Mohammadi and 
                 Dalibor Petkovic and Ch Sudheer",
  title =        "A support vector machine-firefly algorithm-based model
                 for global solar radiation prediction",
  journal =      "Solar Energy",
  volume =       "115",
  pages =        "632--644",
  year =         "2015",
  ISSN =         "0038-092X",
  DOI =          "doi:10.1016/j.solener.2015.03.015",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0038092X15001334",
  abstract =     "In this paper, the accuracy of a hybrid machine
                 learning technique for solar radiation prediction based
                 on some meteorological data is examined. For this aim,
                 a novel method named as SVM-FFA is developed by
                 hybridizing the Support Vector Machines (SVMs) with
                 Firefly Algorithm (FFA) to predict the monthly mean
                 horizontal global solar radiation using three
                 meteorological parameters of sunshine duration ( n - ),
                 maximum temperature (Tmax) and minimum temperature
                 (Tmin) as inputs. The predictions accuracy of the
                 proposed SVM-FFA model is validated compared to those
                 of Artificial Neural Networks (ANN) and Genetic
                 Programming (GP) models. The root mean square (RMSE),
                 coefficient of determination (R2), correlation
                 coefficient (r) and mean absolute percentage error
                 (MAPE) are used as reliable indicators to assess the
                 models' performance. The attained results show that the
                 developed SVM-FFA model provides more precise
                 predictions compared to ANN and GP models, with RMSE of
                 0.6988, R2 of 0.8024, r of 0.8956 and MAPE of 6.1768 in
                 training phase while, RMSE value of 1.8661, R2 value of
                 0.7280, r value of 0.8532 and MAPE value of 11.5192 are
                 obtained in the testing phase. The results specify that
                 the developed SVM-FFA model can be adjudged as an
                 efficient machine learning technique for accurate
                 prediction of horizontal global solar radiation.",
  keywords =     "genetic algorithms, genetic programming, Support
                 vector machine, Firefly algorithm, Hybrid model, Global
                 solar radiation prediction, Meteorological parameters",
  notes =        "Power Electronics and Renewable Energy Research
                 Laboratory (PEARL), Department of Electrical
                 Engineering, Faculty of Engineering, University of
                 Malaya, 50603 Kuala Lumpur, Malaysia",
}

Genetic Programming entries for Lanre Olatomiwa Saad Mekhilef Shahaboddin Shamshirband Kasra Mohammadi Dalibor Petkovic Sudheer Ch

Citations