Probabilistic forecast of electric vehicle charging demand: analysis of different aggregation levels and energy procurement

Ostermann, Adrian; Haug, Theodor

doi:10.1186/s42162-024-00319-1

Energy Informatics

Table 7 Metrics of the models for the zip codes

From: Probabilistic forecast of electric vehicle charging demand: analysis of different aggregation levels and energy procurement

Zip code	Model	nRMSE*	MASE*	R2**	PS low Q***	PS high Q***	IS***
I	LinR	0.975	1.09	0.351
	Bagging	0.990	1.108	0.331	7.090	27.448	985.517
	GradientB	0.981	1.088	0.343	6.039	24.667	1033.998
	Ada	0.983	1.171	0.341	6.214	27.861	1176.891
	Random forest	1.131	1.232	0.127	125.155	78.103	548.477
	LSTM	1.015	1.138	0.296	252.223	235.476	605.484
	CNN	1.013	1.002	0.300	211.708	235.503	574.080
	NN	1.034	1.132	0.270	458.458	235.482	826.995
II	LinR	0.960	0.94	0.687
	Bagging	0.663	0.684	0.851	2.053	3.936	154.005
	GradientB	0.611	0.657	0.873	2.620	6.178	191.541
	Ada	0.689	0.698	0.839	1.968	4.082	162.425
	Random forest	0.682	0.684	0.842	4.002	7.951	113.861
	LSTM	0.763	0.872	0.802	61.401	99.888	189.507
	CNN	0.703	0.756	0.832	67.554	99.889	194.910
	NN	0.855	0.931	0.751	108.516	99.888	240.200
III	LinR	0.958	1.008	0.375
	Bagging	1.008	1.027	0.309	4.447	11.906	567.686
	GradientB	0.992	0.993	0.330	4.675	12.048	546.991
	Ada	1.005	1.047	0.312	4.196	12.550	633.464
	Random forest	1.013	1.045	0.302	44.326	32.429	299.845
	LSTM	1.059	1.106	0.237	279.547	163.712	509.868
	CNN	1.095	1.048	0.184	294.927	163.713	528.844
	NN	0.965	0.988	0.366	552.837	163.704	814.265
IV	LinR	0.949	1	0.734
	Bagging	0.820	0.823	0.801	5.605	10.621	527.691
	GradientB	0.812	0.849	0.805	6.747	11.513	631.780
	Ada	0.834	0.889	0.795	5.583	11.247	602.754
	Random forest	0.814	0.837	0.804	15.005	20.154	340.988
	LSTM	0.848	0.916	0.788	310.657	264.919	658.047
	CNN	1.005	1.02	0.702	287.591	254.567	623.086
	NN	0.978	1.166	0.717	870.033	263.764	1289.562
V	LinR	0.807	0.837	0.815
	Bagging	0.688	0.652	0.866	5.764	7.758	303.393
	GradientB	0.660	0.666	0.877	5.494	8.427	394.922
	Ada	0.666	0.663	0.874	5.020	8.045	320.075
	Random forest	0.713	0.674	0.856	8.923	13.661	229.896
	LSTM	0.791	0.787	0.823	85.854	209.834	347.807
	CNN	0.928	0.85	0.756	73.755	209.837	343.705
	NN	0.636	0.721	0.885	205.055	209.838	472.293

*Unitless, **in %, ***in W

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