Study on Wind Speed Prediction Based on Rbf Neural Network

Feng Wang; Zhi Zhong Tan; De You Liu; Xiang Dong Qian

doi:10.4028/www.scientific.net/AMM.236-237.741

Paper Titles

The Study on Acceptable Capacity of Wind Power for Gansu Power Grid Based on Power Balance
p.720

Research on the Effect of Governor System to Low-Frequency Oscillation of Power System
p.725

Syntheses and Properties of Pyrrole Derivative as a Cathode Material for Li-Ion Batteries
p.731

Simple and Rapid Synthesis of LiFePO₄/C Composite for Lithium Rechargeable Batteries by Microwave-Solvothermal Method Assisted with Short Time Post-Treatment Process
p.736

Study on Wind Speed Prediction Based on Rbf Neural Network
p.741

Application and Research on Reducing Reaction of Boron Slag in the Steelmaking Engineering
p.747

Research on Model of Solid-Solid Reaction and Experiment
p.751

Thermodynamic Research of Boron Oxide Reducing with Carbothermic Method
p.755

Electric Power Tower Inclination Angle Detection Method Based on SIFT Feature Matching
p.759

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 236-237Study on Wind Speed Prediction Based on Rbf Neural...

Study on Wind Speed Prediction Based on Rbf Neural Network

Abstract:

This paper analyzes the importance of the wind farm wind speed prediction, as well as the different forecasting methods in various fields. And established the RBF neural network forecasting model can forecast one hour ahead of the wind farm wind speed, and the results meet the actual forecast requirements. By reconstructing the wind speed time series, wind speed can be predicted one day in advance, the prediction accuracy and one hour ahead of forecast accuracy is similar. The method can predict a longer period of time the wind speed, and provide important reference for the wind farm power generation scheduling.

You might also be interested in these eBooks

Advanced Technology for Manufacturing Systems and Industry

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 236-237)

Pages:

741-746

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.236-237.741

Citation:

Cite this paper

Online since:

November 2012

Authors:

Feng Wang, Zhi Zhong Tan, De You Liu, Xiang Dong Qian

Keywords:

RBF Neural Network, Time Series, Wind Farm, Wind Speed Prediction

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Deyou Liu, Zhizhong Tan, Feng Wang. Study on Combined System with Wind Power and Pumped Storage Power, J. Water Resources and Power. 2006, 24(6), pp.39-42.

Google Scholar

[2] Bianping Su, Yanping Cao, Ting Wang. Research on grey forecasting model of multi-factor time series, J. Journal of Xi an University of Architecture & Technology (Natural Science Edition). 2007, 39(2), pp.289-292.

Google Scholar

[3] Jian Wang. The prediction of cotton fabric moisture permeability based on grey system theory and RBF neural network, J. Progress in Textile Science & Technology. 2007, (3), pp.40-42.

Google Scholar

[4] Yongshan Xiao, Weiqing Wang, Xiaoping Huo. Study on the Time-series Wind Speed Forecasting of the Wind farm Based on Neural Networks, J. Energy Conservation Technology. 2007, 25(142), pp.106-108.

Google Scholar

[5] Yang Xiao. Wind speed and generated power forecasting in wind farm, D. Master paper of Northeast China Institute of Electric Power Engineering. 2005, pp.9-42.

Google Scholar

[6] Xianrong Huang, Deyou Liu. The treatment of hydroturbine synthetic characteristic curve by radial basis networks, J. Journal of Hydroelectric Engineering. 2007, 26(1), pp.114-118.

Google Scholar

[7] Kaili Zhou, Yaohong Kang. Neural network model and MATLAB program development, M. Tsinghua University Press, Beijing, 2005, pp.203-204.

Google Scholar

[8] Ling Su. Study on port throughput forecast using radial basis functions of neural network, D. Master paper of Shanghai maritime university. 2006, pp.37-42.

Google Scholar