Multi-Step-Ahead Forecasting of Wind Speed Based on EMD-RBF Model

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Abstract:

Wind speed forecasting is critical for wind energy conversion systems. Adaptive and reliable methods and techniques of wind speed forecasting are urgently needed in view of the stochastic nature of wind resource, which is varying from time to time and from site to site. Multi-step-ahead speed forecasting is built with empirical mode decomposition (EMD) method and RBF neural network, which makes use of non-linear and non-stationary signal characteristics. Time series of original wind speed data is decomposed by EMD method. And RBF neural network is used to predict the decomposition of the various components. Experimental results show that the method effectively improves the accuracy and the reliability of wind speed forecasting.

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Periodical:

Advanced Materials Research (Volumes 347-353)

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2219-2222

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Online since:

October 2011

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] G. Li, J. Shi, J. Zhou: Bayesian adaptive combination of short-term wind speed forecasts from neural network models. Renewable Energy Vol.36(2011),pp.352-359

DOI: 10.1016/j.renene.2010.06.049

Google Scholar

[2] E.González-Romera M.A. Jaramillo-Morán,D.Carmona-Fernández:Monthly electric energy demand forecasting with neural networks and Fourier series. Energy Conversion and Management Vol.49(2008),p.3135–3142

DOI: 10.1016/j.enconman.2008.06.004

Google Scholar

[3] D.F. Pan, H. Liu, Y.F. Li: Optimization Algorithm of Short-term Multi-step Wind Speed Forecast. Proceedings of the CSEE Vol.28(2008),pp.87-91

Google Scholar

[4] X. Zhang, L. Yu, S.Y. Wang, K.K. Lai: Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method. Energy Economics Vol. 31(2009),p.768–778

DOI: 10.1016/j.eneco.2009.04.003

Google Scholar

[5] L. Yu, S.Y. Wang, K.K. Lai: Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics Vol.30(2008),p.2623–2635

DOI: 10.1016/j.eneco.2008.05.003

Google Scholar

[6] K.C. Sharm, B. Frieldander: Time-varying autoregressive modeling of a class non-stationary signals, ICASSP Vol.22(1984),pp.1-4

Google Scholar