Research Article
Effective Estimation of Hourly Global Solar Radiation Using Machine Learning Algorithms
Table 13
Comparison of the performance of the present study with various HAGSR estimation models of the previous studies.
| ML methods | Best methods | Time range | Location | Author [Ref.] | Statistical indicators | RMSE | MAE | |
| ANN | ANN | 2001–2007 | La Serena (Chile) | Lazzús et al. [18] | — | — | 0.9437 | ANN | ANN | 02 February–31 May 2011 | Algeria | Hasni et al. [17] | 0.172 | 2.9971 | 0.9999 | MLP | MLP | 2009–2012 | Fez (Morocco) | Ihya et al. [62] | — | — | 0.8896 | MLP-NARX | NARX | 2010–2014 | Fes (Morocco) | Loutfi et al. [32] | — | — | 0.95 | AdaBoost, LR, KNN, CART, SVR, ANN, RD regression | RD regression | 2013–2015 | South Korea | Kim et al. [63] | 577.5 | — | 0.705 | ANN | ANN | 2006–2010 | Ajaccio (Corsica) | Notton et al. [12] | 12.43 | 19.17 | 0.998 | MARS-ANN-LR | ANN | 2010–2016 | Hong Kong | Li et al. [28] | 0.270 | 0.194 | 0.918 | MFFNN-KNN-M5 rules-LibSVM | MFFNN | 2002–2006 | Kahramanmaras-Isparta (Turkey) | Present study | 0.0341 | 0.0508 | 0.9656 |
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