Enhancing energy consumption prediction in smart homes: a convergence-aware federated transfer learning approach | Science and Technology for Energy Transition (STET)

Emerging Advances in Hybrid Renewable Energy Systems and Integration

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Table 4

Comparison of proposed FedDNN and FedLSTM models with existing models in terms of mean squared error.

Algorithm	C1	C2	C3	C4	C5	C6	C7	C8	Average
LinearRegression	134.12 ± 2.50	154.23 ± 2.43	161.34 ± 1.39	144.80 ± 1.49	144.11 ± 1.47	173.59 ± 2.74	133.68 ± 2.12	154.94 ± 2.16	150.10 ± 2.04
LGBMRegressor	26.43 ± 1.29	22.54 ± 2.81	23.15 ± 2.05	16.44 ± 2.20	16.37 ± 1.06	24.34 ± 1.60	24.56 ± 2.61	24.56 ± 2.76	22.30 ± 2.05
XGBRegressor	27.16 ± 2.50	25.22 ± 2.26	24.45 ± 1.25	16.43 ± 2.42	15.29 ± 1.53	23.45 ± 2.33	23.43 ± 1.15	23.43 ± 1.61	22.36 ± 1.88
CatBoostRegressor	26.64 ± 1.10	24.54 ± 2.14	25.65 ± 2.02	17.30 ± 2.74	13.32 ± 1.67	22.65 ± 1.73	24.35 ± 2.92	24.23 ± 2.24	22.33 ± 2.07
SGDRegressor	137.25 ± 1.28	153.23 ± 2.73	164.13 ± 2.74	146.73 ± 2.69	143.12 ± 2.39	171.23 ± 1.91	133.34 ± 1.37	154.45 ± 2.17	150.44 ± 2.16
KernelRidge	143.64 ± 2.95	165.75 ± 1.12	171.56 ± 1.66	152.51 ± 1.47	151.74 ± 2.15	182.43 ± 2.51	142.54 ± 2.37	165.56 ± 2.23	159.47 ± 2.06
BayesianRidge	134.17 ± 1.13	151.34 ± 2.35	163.53 ± 1.86	143.43 ± 2.65	144.11 ± 1.84	171.45 ± 2.95	132.34 ± 2.97	153.34 ± 2.39	149.21 ± 2.27
GradientBoostingRegressor	33.85 ± 2.54	28.62 ± 1.79	31.72 ± 2.81	22.37 ± 2.80	23.34 ± 2.37	34.34 ± 1.67	31.34 ± 2.06	32.65 ± 2.83	29.78 ± 2.36
SVR	135.34 ± 4.69	156.35 ± 2.86	165.15 ± 2.83	141.54 ± 1.58	142.12 ± 2.97	171.34 ± 1.58	133.45 ± 1.79	156.58 ± 2.49	150.23 ± 2.60
DNN	24.35 ± 2.01	24.56 ± 2.00	24.67 ± 2.97	16.23 ± 2.96	15.57 ± 1.73	25.07 ± 1.62	23.52 ± 1.00	23.53 ± 1.21	22.19 ± 1.94
FedDNN-full	23.62 ± 2.64	22.42 ± 1.43	23.73 ± 2.69	14.34 ± 2.60	15.24 ± 1.49	24.67 ± 1.48	22.87 ± 2.01	22.72 ± 2.50	21.20 ± 2.11
FedDNN-par	21.58 ± 1.61	17.15 ± 2.75	22.25 ± 1.46	11.24 ± 1.19	12.64 ± 1.69	23.96 ± 2.44	22.30 ± 2.75	20.32 ± 1.63	18.93 ± 1.94
LSTM	31.15 ± 2.61	22.93 ± 1.13	25.14 ± 1.24	16.45 ± 2.77	15.52 ± 2.30	24.46 ± 1.60	23.15 ± 2.83	23.37 ± 2.32	22.77 ± 2.10
FedLSTM-full	28.64 ± 2.29	22.56 ± 1.78	24.56 ± 1.46	14.34 ± 1.91	14.13 ± 1.13	22.58 ± 2.09	22.75 ± 1.75	22.67 ± 2.14	21.53 ± 1.82
FedLSTM-par	19.23 ± 2.75	18.15 ± 1.25	23.65 ± 2.70	12.22 ± 1.54	13.34 ± 1.71	19.49 ± 1.56	21.32 ± 2.90	19.15 ± 1.84	18.32 ± 2.03

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