Research Article
A Novel Deep Learning-Based Data Analysis Model for Solar Photovoltaic Power Generation and Electrical Consumption Forecasting in the Smart Power Grid
Table 6
Comparison with relevant recent works of the literature.
| Model | MSE | RMSE | MAE | MAPE (%) | R | ANOVA | Authors |
| Deep reservoir architecture | 2.15 | 1.466 | 5.42 | 0.64 | 0.8896 | — | [55] | GC-LSTM | 3.66 | 1.913 | 7.85 | 2.36 | 0.8795 | — | [56] | ADDPG-AEFRIM | 2.04 | 1.428 | 2.58 | 0.94 | 0.8996 | — | [57] | TgDLF, EnLSTM | 1.58 | 1.241 | 1.33 | 0.89 | 0.9654 | — | [58] | CNN, DNN, GRU-FCL, LSTM-FCL, Bi-GRU-FCL | 0.06 | 0.244 | 0.48 | 0.75 | 0.9788 | — | [59] | ANFIS, grey, PSO | 0.04 | 0.201 | 0.22 | 0.6 | 0.9969 | — | [41] | k-means, QRLSTM, KDE | 0.012 | 0.11 | 0.15 | 0.47 | 0.9979 | — | [60] | CNN-LSTM | — | — | 0.04 | 0.38 | 0.9987 | — | [61] | TCN-DNN | 0.0035 | 0.059 | — | — | 0.9995 | — | [62] | MLP-LSTM | 0.0023 | 0.047 | 0.01 | 0.1 | 0.9998 | 0.214 | Writers | MLP-LSTM-GA | 0.00012 | 0.002 | 0.005 | 0.014 | 0.9999 | 0.163 | Writers |
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