. 2023 Jul 22;26(8):107456. doi: 10.1016/j.isci.2023.107456

Table 3.

Performance comparison of CDbARNN with other methods

Evaluation metric	Forecasting method	Scenario 1			Scenario 2
Evaluation metric	Forecasting method	I	II	III	I	II	III
NMAE (%)	CDbARNN	3.86	3.98	5.33	11.44	16.11	19.01
	GRNN	5.86	9.54	15.56	13.53	16.55	22.29
	Elman	5.36	13.67	12.13	17.17	20.16	21.39
	BP	8.06	8.11	15.58	17.23	20.02	20.89
NRMSE(%)	CDbARNN	5.87	5.83	7.67	14.47	19.83	23.20
	GRNN	7.89	12.05	20.25	16.80	19.99	27.26
	Elman	7.23	18.56	17.56	22.07	25.10	26.17
	BP	11.26	10.54	21.57	22.56	24.55	26.45
MAPE(%)	CDbARNN	11.67	11.71	14.78	54.75	66.98	73.36
	GRNN	15.96	31.96	43.67	57.41	76.70	91.84
	Elman	16.53	29.82	26.96	55.56	73.75	73.97
	BP	21.25	30.21	40.91	56.51	71.32	79.87
IA	CDbARNN	0.98	0.98	0.93	0.69	0.62	0.42
	GRNN	0.94	0.90	0.68	0.62	0.58	0.40
	Elman	0.95	0.75	0.66	0.57	0.51	0.40
	BP	0.90	0.94	0.64	0.58	0.59	0.41

Through the forecasting results and errors in Figures 9 and 10, it can be observed that CDbARNN is closer to the true value in Scenario 1, and in Scenario 2 it can forecast the actual load trend comparing with GRNN, Elman and BP.