A Simple Hybrid Model for Short-Term Load Forecasting

Table 3

Forecasting accuracy metrics for 4-hr-ahead forecast.

Day	Proposed method		DEM
	RMSE	MAPE	RMSE	MAPE
15th
Summer	0.2657	1.2575	0.4448	2.2143
Spring	0.1934	0.8417	0.4359	0.8325
Winter	0.1623	0.7955	0.4017	1.8907
22nd
Summer	0.1885	0.9445	0.5145	2.4405
Spring	0.1521	0.6962	0.4598	1.9832
Winter	0.1794	0.8262	0.4392	2.0424
29th
Summer	0.1467	0.7307	0.4710	2.2014
Spring	0.1802	0.7902	0.4809	1.9876
Winter	0.2787	1.305	0.5234	2.4684
36th
Summer	0.1712	0.8445	0.5418	2.4261
Spring	0.2558	1.1315	0.5949	2.5174
Winter	0.128	0.6315	0.4594	2.0987
43rd
Summer	0.1511	0.7337	0.5046	2.2080
Spring	0.2406	1.0435	0.6057	2.5245
Winter	0.2288	1.0105	0.4926	2.2277
50th
Summer	0.1290	0.6277	0.4629	0.9494
Spring	0.2646	1.1047	0.6031	2.3827
Winter	0.1725	0.7425	0.4728	2.0265
57th
Summer	0.1761	0.799	0.4430	2.0739
Spring	0.2568	1.0695	0.5661	2.2892
Winter	0.1511	0.7272	0.2321	0.9605
64th
Summer	0.2265	1.0865	0.4518	2.0590
Spring	0.1937	0.836	0.5109	2.1099
Winter	0.1790	0.9077	0.4198	1.9776
71st
Summer	0.1029	0.5025	0.5640	2.4710
Spring	0.2164	0.925	0.5596	2.2997
Winter	0.1698	0.8467	0.5577	2.6100
78th
Summer	0.2512	1.2687	0.5116	2.3770
Spring	0.1573	0.7472	0.3502	1.5144
Winter	0.1781	0.8737	0.3948	1.7977
85th
Summer	0.2071	0.969	0.4885	2.1521
Spring	0.2105	0.983	0.6110	2.6769
Winter	0.1631	0.797	0.3001	1.3905
92nd
Summer	0.1225	0.5535	0.4293	1.8439
Spring	0.2168	0.9395	0.4838	2.0605
Winter	0.1736	0.7222	0.3972	1.6758
99th
Summer	0.1945	0.9087	0.6095	2.5899
Spring	0.1505	0.7075	0.4343	1.9034
Winter	0.2741	1.2167	0.4307	1.9024
106th
Summer	0.1785	0.8467	0.4720	2.1271
Spring	0.1677	0.783	0.5330	2.1665
Winter	0.1809	0.914	0.2348	1.0906