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2013 | OriginalPaper | Chapter

12. Artificial Neural Network Based Methodologies for the Estimation of Wind Speed

Authors : Despina Deligiorgi, Kostas Philippopoulos, Georgios Kouroupetroglou

Published in: Assessment and Simulation Tools for Sustainable Energy Systems

Publisher: Springer London

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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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Title: Artificial Neural Network Based Methodologies for the Estimation of Wind Speed
Authors: Despina Deligiorgi
Kostas Philippopoulos
Georgios Kouroupetroglou
Publisher: Springer London
Book: Assessment and Simulation Tools for Sustainable Energy Systems
Print ISBN: 978-1-4471-5142-5

Electronic ISBN: 978-1-4471-5143-2

Copyright Year: 2013
DOI: https://doi.org/10.1007/978-1-4471-5143-2_12

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