Research and Application of a New Hybrid Forecasting Model Based on Genetic Algorithm Optimization: A Case Study of Shandong Wind Farm in China

Table 1

The forecasting results of four traditional models for original data.

Data	Time	Actual value (m/s)	FAC ( = 0.2)		SAC ( = 0.2)		BP		ARIMA
			Forecasting value (m/s)	MAPE (%)	Forecasting value (m/s)	MAPE (%)	Forecasting value (m/s)	MAPE (%)	Forecasting value (m/s)	MAPE (%)
Observation site 1	0:00	6.7	6.63	1.03	6.69	0.15	7.24	8.03	7.18	7.18
	2:00	8.4	7.76	7.58	7.74	7.81	8.00	4.80	7.96	5.29
	4:00	8.5	7.86	7.48	7.84	7.77	8.00	5.92	8.00	5.91
	6:00	6.9	7.14	3.52	7.15	3.55	7.08	2.68	7.22	4.57
	8:00	6.8	6.67	1.92	6.73	1.02	6.16	9.34	6.28	7.70
	10:00	6.9	6.86	0.53	6.80	1.51	7.16	3.73	7.15	3.58
	12:00	8.3	7.75	6.66	7.73	6.84	7.50	9.61	7.42	10.59
	14:00	10.6	10.67	0.63	10.67	0.65	10.47	1.19	10.26	3.23
	16:00	9.6	9.79	1.96	9.81	2.18	10.42	8.56	9.89	2.98
	18:00	11.7	11.81	0.94	10.67	8.77	11.66	0.36	11.96	2.20
	20:00	10.3	10.52	2.10	10.52	2.16	10.41	1.04	10.06	2.38
	22:00	12	9.41	21.58	9.41	21.57	9.90	17.52	9.82	18.19
Observation site 2	0:00	7.3	6.56	10.09	6.55	10.25	6.69	8.31	6.57	10.03
	2:00	8.5	8.17	3.88	8.16	3.96	8.29	2.47	8.36	1.59
	4:00	8.1	7.96	1.71	7.90	2.43	8.10	0.04	7.90	2.43
	6:00	8	8.35	4.39	8.32	4.04	8.41	5.14	8.28	3.52
	8:00	7.2	6.48	9.99	6.49	9.84	6.62	8.11	6.98	3.07
	10:00	7	7.01	0.15	6.96	0.56	7.34	4.90	6.79	3.03
	12:00	8.4	7.74	7.85	7.73	7.97	7.21	14.21	7.36	12.38
	14:00	11.6	10.65	8.16	10.61	8.51	10.83	6.65	10.58	8.78
	16:00	8.9	9.59	7.74	9.63	8.16	9.53	7.12	10.02	12.56
	18:00	10.9	10.02	8.03	10.04	7.85	11.19	2.68	10.72	1.61
	20:00	10.6	11.19	5.54	11.21	5.72	10.73	1.21	11.01	3.91
	22:00	11.4	12.27	7.60	12.31	8.02	12.52	9.79	12.19	6.93
Observation site 3	0:00	8.3	9.34	12.49	9.37	12.83	8.91	7.32	8.89	7.05
	2:00	9.4	8.86	5.69	8.84	5.92	9.20	2.13	8.99	4.35
	4:00	9.2	9.24	0.45	9.21	0.09	9.46	2.85	9.36	1.79
	6:00	8.1	8.15	0.59	8.15	0.65	8.29	2.29	8.15	0.56
	8:00	8.3	8.47	2.04	8.47	2.00	8.69	4.74	8.50	2.40
	10:00	7.1	7.82	10.08	7.79	9.65	7.84	10.38	7.88	10.93
	12:00	8.8	8.68	1.35	8.68	1.40	9.01	2.33	8.71	0.98
	14:00	10.3	9.90	3.91	9.94	3.52	9.86	4.28	9.38	8.95
	16:00	10.1	11.04	9.31	11.01	9.04	11.97	18.55	11.57	14.57
	18:00	12.2	11.23	7.94	11.22	8.05	11.60	4.91	11.42	6.37
	20:00	12.2	11.62	4.79	11.61	4.87	11.68	4.29	11.82	3.14
	22:00	11	10.74	2.32	10.70	2.72	11.48	4.33	11.26	2.37