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Assessment and Simulation Tools for Sustainable Energy Systems pp 247–266Cite as

Artificial Neural Network Based Methodologies for the Estimation of Wind Speed

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Part of the book series: Green Energy and Technology ((GREEN,volume 129))

Abstract

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.

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Authors and Affiliations

  1. Department of Physics Division of Environmental Physics and Meteorology, University of Athens, Panepistimioupolis, Ilisia, 15784, Athens, Greece

    Despina Deligiorgi

  2. Department of Informatics and Telecommunications, Division of Signal Processing and Communication, University of Athens, Panepistimioupolis, Ilisia, 15784, Athens, Greece

    Georgios Kouroupetroglou

  3. Division of Environmental Physics-Meteorology, Department of Physics, University of Athens, Panepistimioupolis, Ilisia, 15784, Athens, Greece

    Kostas Philippopoulos

Authors
  1. Despina Deligiorgi
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  2. Kostas Philippopoulos
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  3. Georgios Kouroupetroglou
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Correspondence to Despina Deligiorgi .

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Editors and Affiliations

  1. Department of Economics, Management and Society and Institutions, University of Molise, Campobasso, Italy

    Fausto Cavallaro

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Deligiorgi, D., Philippopoulos, K., Kouroupetroglou, G. (2013). Artificial Neural Network Based Methodologies for the Estimation of Wind Speed. In: Cavallaro, F. (eds) Assessment and Simulation Tools for Sustainable Energy Systems. Green Energy and Technology, vol 129. Springer, London. https://doi.org/10.1007/978-1-4471-5143-2_12

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  • DOI: https://doi.org/10.1007/978-1-4471-5143-2_12

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  • Print ISBN: 978-1-4471-5142-5

  • Online ISBN: 978-1-4471-5143-2

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