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.
Item
RMSE
MAE
RMAE
Hn
Et
RBF(3)
O
1.6044
1.0931
0.1094
NA
132.122945
M
1.0887
0.6336
0.0638
NA
110.138815
W
3.3950
2.3570
0.2375
NA
113.548014
PF(3)
O
1.6044
1.0931
0.1094
NA
137.698291
M
1.0887
0.6336
0.0638
NA
112.438881
W
3.3950
2.3570
0.2375
NA
113.008574
RBF(6)
O
1.5997
1.0888
0.1090
NA
374.338732
M
1.0743
0.5767
0.0581
NA
309.984871
W
3.4590
2.4161
0.2435
NA
288.569125
PF(6)
O
1.5997
1.0888
0.1090
NA
346.989140
M
1.0743
0.5767
0.0581
NA
280.287914
W
3.4590
2.4161
0.2435
NA
284.077991
RBF(10)
O
1.6001
1.0892
0.1090
NA
624.630533
M
1.0782
0.5872
0.0591
NA
501.316555
W
3.4590
2.4161
0.2435
NA
368.402477
PF(10)
O
1.6001
1.0892
0.1090
NA
623.420666
M
1.0782
0.5872
0.0591
NA
512.878352
W
3.4590
2.4161
0.2435
NA
501.985355
MLP neural network
11.6547
9.6265
0.9631
20
97.536770
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.
