Short-Term Wind Speed Prediction on Base of Improved Least Squares Support Vector Machine

Zheng Zhao; Long Xin Zhang; Hai Tao  Liu; Zi Rui Liu

doi:10.4028/www.scientific.net/AMM.599-601.1972

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 599-601Short-Term Wind Speed Prediction on Base of...

Short-Term Wind Speed Prediction on Base of Improved Least Squares Support Vector Machine

Abstract:

Accurate wind speed prediction is of significance to improve the ability to coordinate operation of a wind farm with a power system and ensure the safety of power grid operation. According to the randomness and volatility of wind speed, it is put forward that a WD_GA_LS_SVM short-term wind speed combination prediction model on basis of Wavelet decomposition (WD), Genetic alogorithms (GA) optimization and Least squares support vector machine (LS_SVM). Short-term wind speed prediction is carried out and compared with the neural network prediction model with use of the measured data of a wind farm. The results of error analysis indicate the combination prediction model selected is of higher prediction accuracy.

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Periodical:

Applied Mechanics and Materials (Volumes 599-601)

Pages:

1972-1975

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.599-601.1972

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Online since:

August 2014

Authors:

Zheng Zhao*, Long Xin Zhang, Hai Tao Liu, Zi Rui Liu

Keywords:

Genetic Algorithm (GA), Least Squares Support Vector Machine, Wavelet Decomposition, Wind Speed Prediction

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* - Corresponding Author

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