Table 7 Comparison of proposed hybrid approach with other models presented in the literature.
From: Parallel boosting neural network with mutual information for day-ahead solar irradiance forecasting
Ref | Journal publisher | Location | Model | Interval | RMSE | MAE |
|---|---|---|---|---|---|---|
(\(W~m^{-2}\)) | (\(W~m^{-2}\)) | |||||
Energies | Golden, USA | LSTM-RNN | 24hr | 60.31 | 36.9 | |
Applied Sciences | Golden, USA | K-mean-LSTM-RNN | 24hr | 43.41 | 17.6 | |
Proposed study | Â | Golden, USA | PBNN | 24hr | 4.76 | 3.35 |
IEEE-TII | Tripoli, Libya | SCFT-LSTM | 1hr | 13.48 | 10.05 | |
Proposed study | Â | Tripoli, Libya | PBNN | 1hr | 3.92 | 0.80 |
Renewable Energy | JodhPur, India | MEMD-GRU | 1hr | 31.92 | 22.99 | |
Renewable Energy | New Delhi, India | MEMD-GRU | 1hr | 36.25 | 25.65 | |
Proposed study | Â | JodhPur, India | PBNN | 1hr | 11.06 | 4.12 |
Proposed study | Â | New Delhi, India | PBNN | 1hr | 15.55 | 4.74 |
