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

Table 5

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

Meter	Method	RMSE	MAE	R^2
M1
	DB	5.1093	1.6630	0.9245
	HC	5.3902	1.8563	0.9160
	kM	5.2176	1.8483	0.9213
M2
	DB	10.8719	2.5343	0.8524
	HC	10.9243	2.7631	0.8510
	kM	10.6448	2.3971	0.8585
M3
	DB	4.6532	0.9554	0.9340
	HC	4.6407	1.3267	0.9344
	kM	4.9519	1.3331	0.9253
M4
	DB	10.1662	2.3978	0.9857
	HC	11.5014	2.7697	0.9817
	kM	10.9031	2.7014	0.9836
M5
	DB	6.8727	1.6164	0.9757
	HC	6.9378	1.3631	0.9752
	kM	7.2575	1.4105	0.9729
M6
	DB	4.6280	0.8201	0.9617
	HC	4.6956	0.9210	0.9606
	kM	4.9537	1.2178	0.9561
M7
	DB	7.9172	1.3358	0.9448
	HC	7.9193	1.7544	0.9448
	kM	7.9968	1.1567	0.9437
M8
	DB	4.1679	1.1607	0.9228
	HC	4.2259	0.9397	0.9207
	kM	4.1785	0.7979	0.9224
M9
	DB	4.1845	1.6167	0.8948
	HC	4.2176	1.6730	0.8931
	kM	4.2386	1.7501	0.8921
M10
	DB	5.5571	1.3467	0.9563
	HC	5.8465	1.6683	0.9516
	kM	5.6554	1.2593	0.9547

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.