Journal of Applied Mathematics / 2014 / Article / Tab 4

Research Article

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

Table 4

RMSE, MAE, and RMAE refer to SVR and MLP.

ItemRMSEMAERMAEHnEt

RBF(3)O1.60441.09310.1094NA132.122945
M1.08870.63360.0638NA110.138815
W3.39502.35700.2375NA113.548014

PF(3)O1.60441.09310.1094NA137.698291
M1.08870.63360.0638NA112.438881
W3.39502.35700.2375NA113.008574

RBF(6)O1.59971.08880.1090NA374.338732
M1.07430.57670.0581NA309.984871
W3.45902.41610.2435NA288.569125

PF(6)O1.59971.08880.1090NA346.989140
M1.07430.57670.0581NA280.287914
W3.45902.41610.2435NA284.077991

RBF(10)O1.60011.08920.1090NA624.630533
M1.07820.58720.0591NA501.316555
W3.45902.41610.2435NA368.402477

PF(10)O1.60011.08920.1090NA623.420666
M1.07820.58720.0591NA512.878352
W3.45902.41610.2435NA501.985355

MLP neural network11.65479.62650.96312097.536770

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); Hn: number of hidden layer; Et: elapsed time in seconds; RMSE: regression mean squared error for testing sample; MAE: MAE for testing sample; RMAE: RMAE for testing sample; NA: not available.

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