Predicting the wind power density based upon extreme learning machine

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@Article{Mohammadi:2015:Energy,
  author =       "Kasra Mohammadi and Shahaboddin Shamshirband and 
                 Por Lip Yee and Dalibor Petkovic and Mazdak Zamani and 
                 Sudheer Ch",
  title =        "Predicting the wind power density based upon extreme
                 learning machine",
  journal =      "Energy",
  volume =       "86",
  pages =        "232--239",
  year =         "2015",
  ISSN =         "0360-5442",
  DOI =          "doi:10.1016/j.energy.2015.03.111",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0360544215004600",
  abstract =     "Precise predictions of wind power density play a
                 substantial role in determining the viability of wind
                 energy harnessing. In fact, reliable prediction is
                 particularly useful for operators and investors to
                 offer a secure situation with minimal economic risks.
                 In this paper, a new model based upon ELM (extreme
                 learning machine) is presented to estimate the wind
                 power density. Generally, the two-parameter Weibull
                 function has been normally used and recognized as a
                 reliable method in wind energy estimations for most
                 windy regions. Thus, the required data for training and
                 testing were extracted from two accurate Weibull
                 methods of standard deviation and power density. The
                 validity of the ELM model is verified by comparing its
                 predictions with SVM (Support Vector Machine), ANN
                 (Artificial Neural Network) and GP (Genetic
                 Programming) techniques. The wind powers predicted by
                 all approaches are compared with those calculated using
                 measured data. Based upon simulation results, it is
                 demonstrated that ELM can be used effectively in
                 applications of wind power predictions. In a nutshell,
                 the survey results show that the proposed ELM model is
                 suitable and precise to predict wind power density and
                 has much higher performance than the other approaches
                 examined in this study.",
  keywords =     "genetic algorithms, genetic programming, Wind power
                 density, ELM (extreme learning machine), Weibull
                 method, Prediction",
  notes =        "Faculty of Mechanical Engineering, University of
                 Kashan, Kashan, Iran",
}

Genetic Programming entries for Kasra Mohammadi Shahaboddin Shamshirband Por Lip Yee Dalibor Petkovic Mazdak Zamani Sudheer Ch

Citations