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.

Journal of Applied Mathematics

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

Table 4