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

Table 3

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

Meter	Method	RMSE	MAE	R^2
M1
	DB	177.7155	120.7909	0.6172
	HC	144.8932	73.3900	0.7456
	kM	147.2462	77.8078	0.7372
M2
	DB	229.1446	119.5351	0.5278
	HC	182.6804	80.4405	0.6999
	kM	162.8930	77.9993	0.7614
M3
	DB	190.1826	104.0214	0.4226
	HC	153.1764	77.6672	0.6254
	kM	168.7235	94.0590	0.5455
M4
	DB	855.2163	313.5884	0.5228
	HC	676.5370	210.2765	0.7014
	kM	727.3272	235.0870	0.6548
M5
	DB	416.8174	129.1178	0.5846
	HC	390.0882	134.7437	0.6362
	kM	383.4103	124.1867	0.6485
M6
	DB	225.7441	85.4364	0.5291
	HC	177.9789	66.9214	0.7073
	kM	194.6913	93.9808	0.6498
M7
	DB	338.4739	166.1670	0.4788
	HC	224.2768	82.0394	0.7712
	kM	213.3479	74.5784	0.7929
M8
	DB	136.1172	81.3379	0.5331
	HC	95.7162	34.4682	0.7691
	kM	96.7354	28.9196	0.7642
M9
	DB	114.5623	57.0771	0.6771
	HC	75.4612	32.1241	0.8599
	kM	71.2364	29.4829	0.8752
M10
	DB	259.4291	114.6123	0.5876
	HC	171.0675	75.5993	0.8207
	kM	191.8339	97.0186	0.7745

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.