Paper Title:
Wind Speed Prediction Model Based on Radial Basis Functional Neural Network
  Abstract

Wind speed forecasting is important to operation of wind power plants and power systems. To solve short term wind speed prediction problem, a radial basis functional neural network prediction model for wind speed time series based on cross iterative fuzzy clustering algorithm and regularized orthogonal least squares algorithm is proposed. First, the optimal fuzzy clustering centers of samples are computed by cross iterative fuzzy clustering algorithm. Then radial basis functional centers are optimized by regularized orthogonal least squares algorithm, and the generalized cross-validation is regarded as criteria to halt center selection. The proposed model centralizes advantages of both algorithms, and it can decrease network scale, improve generalization performance, accelerate network training speed and avoid ill-conditioning of learning problems. A case of practical wind speed time series from wind power plants verifies validity of the proposed model.

  Info
Periodical
Advanced Materials Research (Volumes 383-390)
Chapter
Chapter 22: Computer-Aided Engineering in Manufacturing
Edited by
Wu Fan
Pages
5656-5662
DOI
10.4028/www.scientific.net/AMR.383-390.5656
Citation
T. Ding, H. F. Xiao, "Wind Speed Prediction Model Based on Radial Basis Functional Neural Network", Advanced Materials Research, Vols. 383-390, pp. 5656-5662, 2012
Online since
November 2011
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Price
$32.00
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