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.
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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)
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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
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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
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