|Table of Contents|

A Wind Speed Forecasting Model Based on Support VectorRegression with Data Dependent Kernel(PDF)

《南京师大学报(自然科学版)》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2014年03期
Page:
15-
Research Field:
计算机科学
Publishing date:

Info

Title:
A Wind Speed Forecasting Model Based on Support VectorRegression with Data Dependent Kernel
Author(s):
Wang DingchengNi YujiaChen BeijingCao Zhili
College of Computer and Software,Nanjing University of Information Science and Technology,Nanjing 210044,China
Keywords:
wind speed forecastingdata dependent kernelsupport vector regression machine
PACS:
TP181
DOI:
-
Abstract:
Wind is random and has many factors.Besides,the prediction accuracy of wind is not high.Therefore,based on the statistics relationship between Support Vector Machine(SVM)and information geometry,the geometry of kernel function is analyzed.A data dependent kernel is constructed and combined with Support Vector Regression(SVR).Then,the support vector regression machine with data dependent kernel is proposed.We build a wind speed forecasting model and forecast the wind speed.Compared with SVM and neural networks,DDK-SVR method has higher prediction accuracy.

References:

[1] 范伟,赵书强,胡炳杰.应用STATCOM提高风电场的电压稳定性[J].电网与清洁能源,2009,25(4):40-44.
[2]Firat U,Engin S N,Saraclar M,et al.Wind speed forecasting based on second order blind identification and autoregressive model[C]//International Conference on Machine Learning and Applications(ICMLA).Washington,DC,USA,2010:686-691.
[3]Babazadeh H,Gao Wenzhong,Lin Cheng,et al.An hour ahead wind speed prediction by Kalman filter[C]//Power Electronics and Machines in Wind Applications(PEMWA).Denver,USA,2012:1-6.
[4]Huang Chiyo,Liu Yuwei,T Weichang,et al.Short term wind speed predictions by using the grey prediction model based forecast method[C]//Green Technologies Conference(IEEE-Green).Baton Rouge,Louisiana,2011:1-5.
[5]Ghanbarzadeh A,Noghrehabadi A R,Behrang M A,et al.Wind speed prediction based on simple meteorological data using artificial neural network[C]//IEEE International Conference on Industrial Informatics.Cardiff,Wales,2009:664-667.
[6]Adam Mirecki,Xavier Roboam,Frederic Richardean.Architecture complexity and energy efficiency of small wind turbines[J].IEEE Transactions on Industrial Electronics,2007,54(1):660-670.
[7]Vapnik V N.The Nature of Statistical Learning Theory[M].New York:Springer-Verlag,1995.
[8]Liu H,Yaonan Wang,Xiaofen Lu.A method to choose kernel function and its parameters for support vector machines[C]//Machine Learning and Cybernetics.Guangzhou,China,2005:4 277-4 280.
[9]Ming-Yuan Cho,Tsair-Fwu Lee,Shih-Wei Kau,et al.Fault diagnosis of power transformers using SVM/ANN with clonal selection algorithm for features and kernel parameters selection[C]//Innovative Computing,Information and Control.Beijing,China,2006:26-30.
[10]Bai Jing,Guo Yueling.Speech recognition method based on linear descending inertia weight PSO algorithm optimizing SVM kernel parameters[J].Natural Computation,2009:565-568.
[11]William M Boothby.An Introduction to Differentiable Manifolds and Riemannian Geometry,Revised Second Edition[M].Singapore:Elsevier,2007.

Memo

Memo:
-
Last Update: 2014-09-30