Extreme learning approach with wavelet transform function for forecasting wind turbine wake effect to improve wind farm efficiency

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@Article{Mladenovic:2016:AES,
  author =       "Igor Mladenovic and Dusan Markovic and 
                 Milos Milovancevic and Miroljub Nikolic",
  title =        "Extreme learning approach with wavelet transform
                 function for forecasting wind turbine wake effect to
                 improve wind farm efficiency",
  journal =      "Advances in Engineering Software",
  volume =       "96",
  pages =        "91--95",
  year =         "2016",
  ISSN =         "0965-9978",
  DOI =          "doi:10.1016/j.advengsoft.2016.02.011",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0965997816300588",
  abstract =     "A wind turbine operating in the wake of another
                 turbine and has a reduced power production because of a
                 lower wind speed after rotor. The flow field in the
                 wake behind the first row turbines is characterized by
                 a significant deficit in wind velocity and increased
                 levels of turbulence intensity. To maximize the wind
                 farm net profit, the number of turbines installed in
                 the wind farm should be different in depend on wind
                 farm project investment parameters. Therefore modelling
                 wake effect is necessary because it has a great
                 influence on the actual energy output of a wind farm.
                 In this paper, the extreme learning machine (ELM)
                 coupled with wavelet transform (ELM-WAVELET) is used
                 for the prediction of wind turbine wake effect in wind
                 far. Estimation and prediction results of ELM-WAVELET
                 model are compared with the ELM, genetic programming
                 (GP), support vector machine (SVM) and artificial
                 neural network (ANN) models. The following error and
                 correlation functions are applied to evaluate the
                 proposed models: Root Mean Square Error (RMSE),
                 Coefficient of Determination (R2) and Pearson
                 coefficient (r). The experimental results show that an
                 improvement in predictive accuracy and capability of
                 generalization can be achieved by ELM-WAVELET approach
                 (RMSE = 0.269) in comparison with the ELM (RMSE =
                 0.27), SVM (RMSE = 0.432), ANN (RMSE = 0.432) and GP
                 model (RMSE = 0.433).",
  keywords =     "genetic algorithms, genetic programming, Wind turbine,
                 Wake model, Wind speed, Soft computing, Forecasting",
  notes =        "University of Nis, Faculty of Economics, Trg kralja
                 Aleksandra 11, Serbia",
}

Genetic Programming entries for Igor Mladenovic Dusan Markovic Milos Milovancevic Miroljub Nikolic

Citations