Application of Fuzzy and Conventional Forecasting Techniques to Predict Energy Consumption in Buildings

Table 4

Comparison of the different clustering techniques for the fuzzy-oriented approach with LSTM on a daily basis.

Meter	Method	RMSE	MAE	R^2
M1
	DB	204.6027	94.4456	0.4926
	HC	195.5054	92.9729	0.5367
	kM	216.1003	109.9226	0.4340
M2
	DB	409.9392	307.3953	−0.5111
	HC	280.1156	121.2162	0.2944
	kM	252.8798	92.4339	0.4250
M3
	DB	275.5571	117.3078	−0.2122
	HC	211.3214	92.0701	0.2871
	kM	222.4379	107.1479	0.2101
M4
	DB	1319.6981	654.4967	−0.1363
	HC	978.9773	214.1039	0.3747
	kM	960.4926	191.4649	0.3981
M5
	DB	544.9214	154.6821	0.2901
	HC	511.4943	114.9144	0.3745
	kM	511.5907	95.2821	0.3743
M6
	DB	232.0024	59.7434	0.5026
	HC	269.5572	64.4206	0.3286
	kM	253.0153	55.5814	0.4085
M7
	DB	425.5167	166.9462	0.1762
	HC	286.3125	98.4231	0.6270
	kM	296.6960	95.4022	0.5995
M8
	DB	170.7479	41.8688	0.2653
	HC	117.1675	29.4378	0.6541
	kM	115.5324	28.0488	0.6637
M9
	DB	207.3880	134.1462	−0.0581
	HC	155.0012	41.3702	0.4089
	kM	128.7002	45.2480	0.5925
M10
	DB	337.4693	87.3844	0.3022
	HC	251.8003	85.1934	0.6115
	kM	274.7407	84.9319	0.5375

Bold value represents the best value among the three rows of each method. DB is DBScan, HC is hierarchical clustering, and kM is k-Means.