NDLI: Wind power forecast using RBF network and culture algorithm

Content Provider	IEEE Xplore Digital Library
Author	Bei Chen Liang Zhao Jian Hong Lu
Copyright Year	2009
Description	Author affiliation: School of Energy & Environment, Southeast University, Nanjing 210096, China (Bei Chen)
Abstract	This paper proposes a novel technique that combines orthogonal least-squares (OLS) and culture algorithm (CA) to construct the radial basis function (RBF) network for the wind power forecast. By reason of the fluctuation and volatility in wind, wind power generations provide a challenge to the security and stability of the electric system, thus the growing revolution in wind energy encourages more accurate prediction models. The RBF network is composed of three-layer structure, which contains the input, hidden, and output layer. To simplify it, the OLS algorithm is used primarily to determine the number of the centers in the hidden layer. The culture algorithm is a class of computational models derived from observing the culture evolution process in nature and has three major components. In this paper, it is used to tune the parameters in the network. The experimental results reveal the effectiveness and accuracy of the above-mentioned approach, implying it can serve as a promising alternative for wind power forecast.
Starting Page	1
Ending Page	6
File Size	379018
Page Count	6
File Format	PDF
ISBN	9781424449347
DOI	10.1109/SUPERGEN.2009.5348174
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fluctuations Stability Wind Power Forecast(WPF) Predictive models Power system security Wind forecasting Power system modeling Wind energy generation Orthogonal Least-Squares (OLS) Culture Algorithm (CA) Wind energy Radial basis function networks Radial Basis Function (RBF) Wind power generation
Content Type	Text
Resource Type	Article

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