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

Table 9

Evaluation result of different grey models in Example B.

Fitting	GM(1,1)	ARGM	DGM	NGM	NIGM	FGM(1,1)	SAIGM	GM(1,1,)
RMSE	8410.121727	4906.499126	8410.286218	51928.17151	5158.480682	1367.489689	1660.996872	18561.19627
MAE	6926.686251	4221.269336	6927.244243	27186.02944	3881.517201	1016.979092	1384.772743	14753.88106
NRMSE	1.79E −05	0.006017121	0.000384048	0.199776443	5.88E −16	0.003063485	1.47E −04	0.108418844
MAPE	1.787683435	1.104264936	1.788930619	7.705950265	1.099328531	0.256381015	0.358317783	3.9624714
RMSPE	2.197483638	1.313602202	2.200080172	15.3330731	1.509443367	0.34767392	0.437458618	5.139941157
MSE	70730147.47	24073733.67	70732914.28	2696534997	26609922.95	1870028.049	2758910.609	344518006.9
IA	0.987856201	0.995717785	0.987850143	0.775004734	0.995552338	0.999687628	0.99954467	0.95091572
U1	0.010871273	0.00635032	0.010870745	0.069089208	0.006667366	0.001768468	0.002146699	0.024435002
U2	0.021739295	0.012682793	0.02173972	0.134228952	0.013334139	0.003534819	0.004293505	0.047978772
Prediction	GM(1,1)	ARGM	DGM	NGM	NIGM	FGM(1,1)	SAIGM	GM(1,1,)
RMSE	29188.27919	7225.052506	29121.06984	34223.58019	13657.87156	17245.03553	11989.99302	6088.66592
MAE	28931.23345	5843.326918	28866.80301	32283.57548	10658.80219	13665.04068	9353.617817	4849.76408
NRMSE	0.111492795	0.013953907	0.111244499	0.124411774	0.041076011	0.052661204	0.03217455	0.00913139
MAPE	6.419544814	1.280694484	6.405345597	7.122972999	2.324557306	2.982593219	2.04044481	1.06500067
RMSPE	6.457863097	1.566430259	6.443180338	7.496943508	2.960127649	3.739148714	2.595513162	1.32349051
MSE	851955642.2	52201383.72	848036708.6	1171253441	186537455.5	297391250.5	143759932.6	37071852.7
IA	0.521410498	0.823769072	0.52205901	0.349931321	0.596904989	0.495386509	0.615535006	0.88095424
U1	0.031445311	0.008068562	0.031375108	0.039484363	0.015373596	0.019477492	0.013461203	0.00678995
U2	0.064920974	0.016070062	0.064771486	0.07612056	0.030378027	0.038356646	0.026668308	0.0135425