A Novel Power-Driven Grey Model with Whale Optimization Algorithm and Its Application in Forecasting the Residential Energy Consumption in China

Table 6

Evaluation result of different grey models in Example A.

Fitting	GM(1,1)	ARGM	DGM	NGM	NIGM	FGM(1,1)	SAIGM	GM(1,1,)
RMSE	23.86274134	27.06135354	23.66839461	118.1206638	24.48123827	24.4715338	23.55366157	28.84466814
MAE	20.25637699	19.68692977	20.89112313	88.25197924	19.59112421	18.18583986	20.50145901	20.47248493
NRMSE	0.007664991	0.024109321	0.00130884	0.262191084	3.09E − 16	0.009028807	6.38E − 05	0.036768004
MAPE	2.28258768	2.120482298	2.336063456	11.47265586	2.156267271	1.934316772	2.275679299	2.351034331
RMSPE	2.668940276	2.90890819	2.674700195	17.69775476	2.656105278	2.605146741	2.617187635	3.280388261
MSE	569.4304242	732.3168555	560.1929035	13952.49123	599.3310274	598.8559665	554.7749735	832.01488
IA	0.998657193	0.998291523	0.998685903	0.969110255	0.998594898	0.998594002	0.998699542	0.998042174
U1	0.01235891	0.013937773	0.012237389	0.063448569	0.012659895	0.012673831	0.012179981	0.014834273
U2	0.024676152	0.027983796	0.024475181	0.122147051	0.025315732	0.025305697	0.024356537	0.029827898
Prediction	GM(1,1)	ARGM	DGM	NGM	NIGM	FGM(1,1)	SAIGM	GM(1,1,)
RMSE	252.8476943	141.1491367	260.5294979	275.1057498	170.7912875	189.0706106	236.7945303	83.450789
MAE	190.0408036	102.9062759	198.9204499	274.0837795	124.4124534	137.7478703	176.7256754	82.8804665
NRMSE	0.179344392	0.08089408	0.187724249	0.258657024	0.106690266	0.120734047	0.166778703	0.07821556
MAPE	9.954008505	5.411556534	10.4350859	14.92924272	6.519477311	7.211856848	9.251358159	4.50171212
RMSPE	13.13711368	7.326292761	13.54158261	14.96064621	8.861865967	9.811145772	12.30065852	4.5142693
MSE	63931.95652	19923.07878	67875.61925	75683.17359	29169.6639	35747.69578	56071.64958	6964.03418
IA	0.593522143	0.786941885	0.580405823	0.462920793	0.731260314	0.699839537	0.616307649	0.83617379
U1	0.06517862	0.037446493	0.067003457	0.080883402	0.044965721	0.049568808	0.061267078	0.02322958
U2	0.137579314	0.076801972	0.141759132	0.149690352	0.092930838	0.102876971	0.128844478	0.04540719