Assessing the proficiency of adaptive neuro-fuzzy system to estimate wind power density: Case study of Aligoodarz, Iran

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@Article{Shamshirband:2016:RSER,
  author =       "Shahaboddin Shamshirband and Afram Keivani and 
                 Kasra Mohammadi and Malrey Lee and Siti Hafizah Abd Hamid and 
                 Dalibor Petkovic",
  title =        "Assessing the proficiency of adaptive neuro-fuzzy
                 system to estimate wind power density: Case study of
                 Aligoodarz, Iran",
  journal =      "Renewable and Sustainable Energy Reviews",
  volume =       "59",
  pages =        "429--435",
  year =         "2016",
  ISSN =         "1364-0321",
  DOI =          "doi:10.1016/j.rser.2015.12.269",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1364032115016524",
  abstract =     "The prime aim of this study is appraising the
                 suitability of adaptive neuro-fuzzy inference framework
                 (ANFIS) to compute the monthly wind power density. On
                 this account, the extracted wind power from Weibull
                 functions are used for training and testing the
                 developed ANFIS model. The proficiency of the ANFIS
                 model is certified by providing thorough statistical
                 comparisons with artificial neural network (ANN) and
                 genetic programming (GP) techniques. The computed wind
                 power by all models are compared with those obtained
                 using measured data. The study results clearly indicate
                 that the proposed ANFIS model enjoys high capability
                 and reliability to estimate wind power density so that
                 it presents high superiority over the developed ANN and
                 GP models. Based upon relative percentage error (RPE)
                 values, all estimated wind power values via ANFIS model
                 are within the acceptable range of -10percent to
                 10percent. Additionally, relative root mean square
                 error (RRMSE) analysis shows that ANFIS model has an
                 excellent performance for estimation of wind power
                 density.",
  keywords =     "genetic algorithms, genetic programming, Wind power
                 prediction, ANFIS, Weibull distribution, Statistical
                 indicators",
}

Genetic Programming entries for Shahaboddin Shamshirband Afram Keivani Kasra Mohammadi Malrey Lee Siti Hafizah Abd Hamid Dalibor Petkovic

Citations