Artificial Neural Network Based Methodologies for the Estimation of Wind Speed

  • Despina DeligiorgiEmail author
  • Kostas Philippopoulos
  • Georgios Kouroupetroglou
Part of the Green Energy and Technology book series (GREEN, volume 129)


Recent advances in artificial neural networks (ANN) propose an alternative promising methodological approach to the problem of time series assessment as well as point spatial interpolation of irregularly and gridded data. In the field of wind power sustainable energy systems ANNs can be used as function approximators to estimate both the time and spatial wind speed distributions based on observational data. The first part of this work reviews the theoretical background, the mathematical formulation, the relative advantages, and limitations of ANN methodologies applicable to the field of wind speed time series and spatial modeling. The second part focuses on implementation issues and on evaluating the accuracy of the aforementioned methodologies using a set of metrics in the case of a specific region with complex terrain. A number of alternative feedforward ANN topologies have been applied in order to assess the spatial and time series wind speed prediction capabilities in different time scales. For the temporal forecasting of wind speed ANNs were trained using the Levenberg–Marquardt backpropagation algorithm with the optimum architecture being the one that minimizes the Mean Absolute Error on the validation set. For the spatial estimation of wind speed the nonlinear Radial basis function Artificial Neural Networks are compared versus the linear Multiple Linear Regression scheme.


Wind Speed Artificial Neural Network Hide Layer Mean Absolute Error Artificial Neural Network Output 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Despina Deligiorgi
    • 1
    Email author
  • Kostas Philippopoulos
    • 3
  • Georgios Kouroupetroglou
    • 2
  1. 1.Department of Physics Division of Environmental Physics and MeteorologyUniversity of AthensAthensGreece
  2. 2.Department of Informatics and Telecommunications, Division of Signal Processing and CommunicationUniversity of AthensAthensGreece
  3. 3.Division of Environmental Physics-Meteorology, Department of PhysicsUniversity of AthensAthensGreece

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