Short-Term Wind Speed Forecasting Using the Data Processing Approach and the Support Vector Machine Model Optimized by the Improved Cuckoo Search Parameter Estimation Algorithm

Table 1

Forecasting error results between SVM and ANN forecasting model.

Horizon	Wind turbine	Error type	SVM	BPNN	Elman NN	WNN	ARIMA	Horizon	Wind turbine	Error type	SVM	BPNN	Elman NN	WNN	ARIMA
1-step-ahead	1#	MSE	0.7306	1.9341	1.5646	2.3015	0.8544	4-step-ahead	1#	MSE	0.8133	2.1649	2.181	3.1542	1.2014
		MAPE	6.72	11.22	11.18	13.71	4.741			MAPE	7.01	12.25	14.03	17.81	6.882
	2#	MSE	0.4339	0.9645	1.1323	2.6048	0.9006		2#	MSE	0.8471	3.2995	2.1403	5.0735	1.5609
		MAPE	5.36	8.87	8.99	13.06	5.889			MAPE	7.58	14.08	12.99	20.23	7.373
	3#	MSE	0.4838	2.5618	1.4261	1.7689	0.9942		3#	MSE	0.8871	3.1718	2.7741	4.7698	1.8086
		MAPE	5.37	13.73	10.65	12.73	6.218			MAPE	7.19	14.28	15.76	19.77	7.280%
	4#	MSE	0.7954	1.8717	1.5713	3.2243	2.4862		4#	MSE	1.4467	3.6119	3.1221	6.4129	1.7779
		MAPE	8.19	12.73	12.11	14.13	7.283			MAPE	11.26	17.69	16.59	19.94	7.821
2-step-ahead	1#	MSE	0.639	1.2661	1.8282	3.6953	1.0663	6-step-ahead	1#	MSE	1.0364	2.708	2.6968	4.1962	1.3271
		MAPE	6.19	9.31	12.70	17.74	5.922			MAPE	8.03	13.56	16.90	18.40	6.709
	2#	MSE	0.5788	1.2924	1.6292	3.2163	1.0885		2#	MSE	1.0511	3.5101	2.5112	5.6064	2.0692
		MAPE	6.31	10.74	10.66	16.69	6.154			MAPE	8.44	14.90	14.27	22.60	7.954
	3#	MSE	0.6497	2.9984	1.8137	2.5426	1.5840		3#	MSE	1.0549	2.6744	2.9361	6.5295	1.3540
		MAPE	6.27	13.72	13.08	13.14	5.648			MAPE	7.84	12.49	16.29	20.57	7.380
	4#	MSE	1.0798	3.8907	1.8167	2.671	1.4168		4#	MSE	1.7788	4.7215	3.7118	4.8947	2.7558
		MAPE	9.58	18.82	13.46	15.94	6.955			MAPE	12.40	22.09	19.21	20.59	10.446