Accurate prediction of different forecast horizons wind speed using a recursive radial basis function neural network

Protection and Control of Modern Power Systems

Table 7 Comparison of the different approaches

S. No	Various Wind Speed Prediction Approaches	Methodology	Year	Performance Metrics	Parametric Values	Training Computation Time in sec
1	Anurag More & Deo M C Approach	NN with Cascade Algorithm	1995	MSE	0.0015	280
2	Perez Li era et al. Approach	NN with BP Algorithm	1998	MSE	6.3334e-04	305
3	Selcuk Nogay H et al. Approach	MLP with BP Algorithm	2012	MSE	1.7244e-05	186
4	Vigneswaran T & Dhivya S Approach	GRNN	2012	MSE	0.0236	253
4	Vigneswaran T & Dhivya S Approach	GRNN	2012	MAPE	1.1545	253
5	Gnana Sheela K & Deepa S N Approach	RBF	2013	MSE	7.1052e-06	152
				MAE	0.0010
				MAPE	0.0131
6	Chuanan Yao et al. Approach	LCWNN	2013	MSE	0.0070	137
				RMSE	0.0834
				MAPE	0.4408
7	Ramesh Babu N & Arulmozhivarman P Approach	WT - NN	2013	MSE	1.0656e-04	148
				RMSE	0.0103
				MAE	0.0029
				MAPE	0.0361
8	Qinghua Hu et al. Approach	Ensemble GPCA	2014	MSE	0.0087	195
9	Ramesh Babu N & Arulmozhivarman P Approach	NARX	2014	MSE	0.0335	252
9	Ramesh Babu N & Arulmozhivarman P Approach	NARX	2014	RMSE	0.1830	252
10	Wenyu Zhang et al. Approach	SSA	2014	MAE	0.0733	224
				RMSE	0.1031
				MAPE	0.9050
11	Madhiarasan M & Deepa S N	IBPN	2015	MSE	5.1014e-05	140
				RMSE	0.0071
				MAE	2.9832e-04
				MRE	3.6823e-05
				MAPE	0.0037
12	Proposed Approach	RRBFNN		MSE	1.6166e-12	72
				RMSE	1.2715e-05
				MAE	8.2352e-07
				MRE	1.0165e-07
				MAPE	1.0165e-05