Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks

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@Article{Zameer:2017:ECM,
  author =       "Aneela Zameer and Junaid Arshad and Asifullah Khan and 
                 Muhammad Asif Zahoor Raja",
  title =        "Intelligent and robust prediction of short term wind
                 power using genetic programming based ensemble of
                 neural networks",
  journal =      "Energy Conversion and Management",
  volume =       "134",
  pages =        "361--372",
  year =         "2017",
  ISSN =         "0196-8904",
  DOI =          "doi:10.1016/j.enconman.2016.12.032",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0196890416311189",
  abstract =     "The inherent instability of wind power production
                 leads to critical problems for smooth power generation
                 from wind turbines, which then requires an accurate
                 forecast of wind power. In this study, an effective
                 short term wind power prediction methodology is
                 presented, which uses an intelligent ensemble regressor
                 that comprises Artificial Neural Networks and Genetic
                 Programming. In contrast to existing series based
                 combination of wind power predictors, whereby the error
                 or variation in the leading predictor is propagated
                 down the stream to the next predictors, the proposed
                 intelligent ensemble predictor avoids this shortcoming
                 by introducing Genetical Programming based
                 semi-stochastic combination of neural networks. It is
                 observed that the decision of the individual base
                 regressors may vary due to the frequent and inherent
                 fluctuations in the atmospheric conditions and thus
                 meteorological properties. The novelty of the reported
                 work lies in creating ensemble to generate an
                 intelligent, collective and robust decision space and
                 thereby avoiding large errors due to the sensitivity of
                 the individual wind predictors. The proposed ensemble
                 based regressor, Genetic Programming based ensemble of
                 Artificial Neural Networks, has been implemented and
                 tested on data taken from five different wind farms
                 located in Europe. Obtained numerical results of the
                 proposed model in terms of various error measures are
                 compared with the recent artificial intelligence based
                 strategies to demonstrate the efficacy of the proposed
                 scheme. Average root mean squared error of the proposed
                 model for five wind farms is 0.117575.",
  keywords =     "genetic algorithms, genetic programming, Wind power
                 forecasting, Meterological variables, Regression,
                 Artificial neural network, Ensemble",
}

Genetic Programming entries for Aneela Zameer Junaid Arshad Asifullah Khan Muhammad Asif Zahoor Raja

Citations