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

Table 2

Comparison between LSTM and MLP models without using the fuzzy approach using the entire series.

Meter	Model	Daily			Hourly
		RMSE	MAE	R^2	RMSE	MAE	R^2
M1
	LSTM	146.1452	64.1385	0.7411	5.3348	1.4093	0.9177
	MLP	125.4831	56.3078	0.8092	5.1306	1.5869	0.9239
M2
	LSTM	223.3225	88.8834	0.5515	10.9791	1.6636	0.8495
	MLP	138.0524	59.2156	0.8286	10.6464	2.9834	0.8584
M3
	LSTM	190.5074	87.7230	0.4206	4.6348	1.0642	0.9345
	MLP	143.4906	51.9965	0.6713	4.7567	1.4236	0.9311
M4
	LSTM	804.5589	170.6163	0.5776	11.1949	2.2656	0.9827
	MLP	666.6745	190.1642	0.7100	10.1513	2.6782	0.9858
M5
	LSTM	447.4029	104.5444	0.5214	6.7374	1.0369	0.9766
	MLP	379.0906	111.1212	0.6564	6.7014	0.9829	0.9769
M6
	LSTM	218.1491	50.4770	0.5603	5.0652	0.6833	0.9541
	MLP	179.5734	50.0577	0.7020	4.8017	1.0286	0.9588
M7
	LSTM	232.0006	61.1003	0.7551	8.1263	0.6860	0.9419
	MLP	202.1236	66.4395	0.8141	7.8352	1.2313	0.9460
M8
	LSTM	98.5282	24.3363	0.7554	4.3384	0.5955	0.9164
	MLP	89.2251	32.0510	0.7994	4.0838	0.8277	0.9259
M9
	LSTM	78.9721	30.2903	0.8466	4.2556	1.5538	0.8912
	MLP	69.3567	31.2242	0.8817	4.1723	1.7256	0.8954
M10
	LSTM	213.9082	52.9707	0.7196	5.7286	1.0394	0.9535
	MLP	163.2353	63.7221	0.8367	5.5033	1.3785	0.9571

Bold value represents the best value between the two rows of each model.