Accurate short-term wind speed prediction by exploiting diversity in input data using banks of artificial neural networks

@article{SalcedoSanz2009AccurateSW,
  title={Accurate short-term wind speed prediction by exploiting diversity in input data using banks of artificial neural networks},
  author={Sancho Salcedo-Sanz and {\'A}ngel M. P{\'e}rez-Bellido and Emilio G. Ort{\'i}z-Garc{\'i}a and Jos{\'e} Antonio Portilla-Figueras and Luis Prieto and Francisco Correoso},
  journal={Neurocomputing},
  year={2009},
  volume={72},
  pages={1336-1341}
}
Wind speed prediction is a very important part of wind parks management. Currently, hybrid physicalstatistical wind speed forecasting models are used to this end, some of them using neural networks as the final step to obtain accurate wind speed predictions. In this paper we propose a method to improve the performance of one of these hybrid systems, by exploiting diversity in the input data of the neural applied with different parameterizations. Two structures of neural network banks are used… CONTINUE READING

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