A Hybrid Forecasting Model Based on Empirical Mode Decomposition and the Cuckoo Search Algorithm: A Case Study for Power Load

Table 5

Comparison of power load forecasting GRA by using different methods from Feb. 2 to Mar. 1.

Time	BPNN	GABPNN	WNN	CSAWN	EMD-CSAWNN	ARIMA
	GRA	GRA	GRA	GRA	GRA	GRA
Feb. 2	0.7962	0.8018	0.7582	0.8186	0.8470	0.7553
Feb. 3	0.7587	0.7532	0.7756	0.7626	0.8121	0.7468
Feb. 4	0.7132	0.7077	0.7216	0.7429	0.7611	0.7076
Feb. 5	0.7225	0.7381	0.7672	0.7717	0.8032	0.7305
Feb. 6	0.7660	0.7991	0.8194	0.7800	0.8598	0.8027
Feb. 7	0.8555	0.8680	0.7952	0.8431	0.8839	0.8381
Feb. 8	0.7507	0.7184	0.7562	0.7504	0.8191	0.7541
Feb. 9	0.7622	0.7682	0.7495	0.7840	0.8352	0.7554
Feb. 10	0.7644	0.8003	0.7750	0.7993	0.8376	0.7573
Feb. 11	0.8297	0.8608	0.8479	0.8035	0.9109	0.8032
Feb. 12	0.7949	0.8509	0.8439	0.8074	0.9018	0.8169
Feb. 13	0.8262	0.8606	0.8396	0.8493	0.9050	0.8156
Feb. 14	0.7628	0.8064	0.7800	0.8067	0.8856	0.8058
Feb. 15	0.7392	0.7565	0.7716	0.7674	0.8262	0.7317
Feb. 16	0.8147	0.8298	0.8093	0.8291	0.8520	0.7859
Feb. 17	0.7927	0.7751	0.7656	0.7755	0.7964	0.7577
Feb. 18	0.7908	0.8114	0.7938	0.8043	0.8400	0.7791
Feb. 19	0.8254	0.8199	0.8227	0.8234	0.8756	0.7884
Feb. 20	0.7249	0.7719	0.7641	0.7901	0.7907	0.7340
Feb. 21	0.8555	0.8756	0.8473	0.8676	0.9334	0.8174
Feb. 22	0.7267	0.7600	0.7412	0.7624	0.8356	0.7547
Feb. 23	0.8258	0.8255	0.7912	0.7951	0.8670	0.7872
Feb. 24	0.7511	0.7630	0.7404	0.7642	0.8316	0.7363
Feb. 25	0.7932	0.7916	0.8259	0.8200	0.8331	0.7758
Feb. 26	0.7628	0.7983	0.7316	0.7402	0.8058	0.7063
Feb. 27	0.7440	0.7870	0.7203	0.7615	0.8257	0.7306
Feb. 28	0.8012	0.8331	0.7970	0.8011	0.8666	0.7607
Mar. 1	0.7681	0.8433	0.7974	0.7667	0.8214	0.7775
Maximum value	0.8555	0.8756	0.8479	0.8676	0.9334	0.8381
Minimum value	0.7132	0.7077	0.7203	0.7402	0.7611	0.7063
Average value	0.8673	0.8811	0.8692	0.8756	0.9127	0.8576