Abstract
In this paper, an advanced system based on artificial intelligence and fuzzy logic techniques is developed to predict the wind power output of a wind farm. A fuzzy logic model is applied first to check the reliability of the numerical weather predictions (NWPs) and to split them in two sub-sets, of good and bad quality NWPs, respectively. Two Radial Basis Function (RBF) neural networks, one for each sub-set are trained next to estimate the wind power. Results from a real wind farm are presented and the added value of the proposed method is demonstrated by comparison with alternative methods.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hatziargyriou, N., Zervos, A.: Wind power development in Europe. Proceedings of the IEEE 89(12), 1765–1782 (2001)
IEA Wind Energy Annual Report (2003)
Wind Energy: The Facts, An Analysis of Wind Energy in the EU-25, EWEA (2004)
Wind Power Outlook (2004), http://www.awea.org
Karayiannis, N.B., Balasubramanian, M., Malki, H.A.: Short-term electric power load forecasting based on cosine radial basis function neural networks: An experimental evaluation. International journal of intelligence systems 20(9), 591–605
Wadge, E., Kodogiannis, V.: Extended Normalised Radial Basis Function for Short Term Load Forecasting. Proc. (429) Modelling, Simulation, and Optimization (2004)
Gontar, Z., Sideratos, G., Hatziargyriou, N.: Short-Term Load Forecasting Using Radial Basis Function Networks. In: Vouros, G.A., Panayiotopoulos, T. (eds.) SETN 2004. LNCS (LNAI), vol. 3025, pp. 432–438. Springer, Heidelberg (2004)
Giebel, G., Landberg, L., Kariniotakis, G., Brownsword, R.: State-of-the-Art on Methods and Software Tools for Short-Term Prediction of Wind Energy Production. In: Proc. of EWEC 2003, Madrid, Spain (2003)
Kariniotakis, G., et al.: What performance can be expected by short-term wind power prediction models depending on site characteristics. In: Proc. of the EWEC 2004, London, UK, November 22-25 (2004)
Madsen, H., Kariniotakis, G., Nielsen, H.Aa., Nielsen, T.S., Pinson, P.: A Protocol for Standardising the Performance Evaluation of Short-Term Wind Power Prediction Models. In: CD-Rom Proceedings of the Global WindPower 2004 Conference, Chicago, Illinois, USA, March 28-31 (2004)
Kariniotakis, G.: Towards Next Generation Sort-term Forecasting of Wind Power. In: The Anemos Team CD-Rom Proceedings of the Global WindPower 2004 Conference, Chicago, Illinois, USA, March 28-31 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sideratos, G., Hatziargyriou, N.D. (2006). Application of Radial Basis Function Networks for Wind Power Forecasting. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_76
Download citation
DOI: https://doi.org/10.1007/11840930_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38871-5
Online ISBN: 978-3-540-38873-9
eBook Packages: Computer ScienceComputer Science (R0)