Research on Particle Swarm Optimization Least Square Support Vector Machine for Short-Term Wind Speed Prediction

Shuai Zhang; Hai Rui Wang; Jin Huang; He Liu

doi:10.4028/www.scientific.net/AMM.511-512.927

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 511-512Research on Particle Swarm Optimization Least...

Research on Particle Swarm Optimization Least Square Support Vector Machine for Short-Term Wind Speed Prediction

Abstract:

In the paper, the forecast problems of wind speed are considered. In order to enhance the redaction accuracy of the wind speed, this article is about a research on particle swarm optimization least square support vector machine for short-term wind speed prediction (PSO-LS-SVM). Firstly, the prediction models are built by using least square support vector machine based on particle swarm optimization, this model is used to predict the wind speed next 48 hours. In order to further improve the prediction accuracy, on this basis, introduction of the offset optimization method. Finally large amount of experiments and measurement data comparison compensation verify the effectiveness and feasibility of the research on particle swarm optimization least square support vector machine for short-term wind speed prediction, Thereby reducing the short-term wind speed prediction error, very broad application prospects.

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Periodical:

Applied Mechanics and Materials (Volumes 511-512)

Pages:

927-930

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.511-512.927

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Online since:

February 2014

Authors:

Shuai Zhang, Hai Rui Wang*, Jin Huang, He Liu

Keywords:

Ls-SVM, Offset Optimization, PSO, Wind Speed Prediction

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* - Corresponding Author

References

[1] Shen Guangping, Qin Lang, Huang Qingan and etc. Research on Low Heat Conductivity Based Thermal Wind Speed and Wind Direction Sensor [J]. Instrument and Meter Academic Journal， 30（5）：984-989. (2009).

Google Scholar

[2] GUSELIA V, MATERAZZI A L. Non-Gausian along-wind response analysis in time and frequency domains[J]. Engineering Structures, 22(1): 49-57. (2000).

DOI: 10.1016/s0141-0296(98)00074-1

Google Scholar

[3] Negnevitsky M, Potter C W. Innovative short-term wind generation prediction techniques [C]. 2006 PSCE Power Systems Conference and Exposition, Atlanta, USA, (2006).

DOI: 10.1109/psce.2006.296250

Google Scholar

[4] Liu Ye, Lu Xiaofen, Fang Ruiming, etc. Overview of Wind Speed Prediction Methods in Wind Power Generation System [J]．Grid and Clean Energy, 26 ( 6) : 62-66. (2010).

Google Scholar