Journal of Applied Mathematics / 2014 / Article / Tab 3

Research Article

Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting

Table 3

Simulation results refer to multiscale SVR.

ItemBCVMBcBgFoinRMRS

RBF(3)O0.0012164210240.0019531334650.003838910.943034
M0.00118972640.01562517560.001949810.972598
W0.002174580.5116280.02113230.897533

PF(3)O0.0012164210240.0019531343650.003838910.943034
M0.00118972640.01562527560.001949810.972598
W0.002174580.5116280.02113230.897533

RBF(6)O0.0011700410240.0039062545620.003816120.94281
M0.00108761280.0039062528100.001898550.972905
W0.00213210.5115340.02193670.895725

PF(6)O0.0011700410240.0039062545620.003816120.94281
M0.00108761280.0039062528100.001898550.972905
W0.00213210.5115340.02193670.895725

RBF(10)O0.00118045120.0019531353660.003818040.94289
M0.001072031280.0039062524410.001912270.972918
W0.002174580.5115340.02193670.895725

PF(10)O0.00118045120.0019531353660.003818040.94289
M0.001072031280.0039062524410.001912270.972918
W0.002143530.5115340.02193670.895725

O: original data; M: MAD filter; W: wavelet filter; RBF(3): number of cross-validation for testing set is 3 by RBF, similar to RBF(6) and RBF(10); PF(3): number of cross-validation for testing set is 3 by PF, similar to PF(6) and PF(10); BCVM: best cross-validation mean squared error; Bc: best c; Bg: best g; Foin: finished optimization iteration number; RM: regression mean squared error; RS: regression squared correlation coefficient.

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