Table 2 State-of-the-art: Data-driven modelling
From: Energy forecasting based on predictive data mining techniques in smart energy grids
Author | Forecast model | Forecast horizon | Performance metrics & forecast error measurement | Contribution & Perspective |
|---|---|---|---|---|
Liu et al. (Liu et al. 2015) | − Back propagation based ANN | 24 h ahead | MAPE = 7.65% | − Aerosol index parameter used as input, which resulted in slightly improved accuracy |
Ramsami, Oree (Ramsami and Oree 2015) | − Stepwise Regression − GRNN − FFNN − MLR | 24 h ahead | RMSE = 2.74% | − Stepwise regression: select I/P variables highly correlated to PV power O/P − GRNN,FFNN,MLR and their hybrid were applied on the I/P − Hybrid model showed slight improvement |
Ordiano et al. (Ordiano et al. 2017) | − ANN6 − ANN10 (Data-driven) | 24 h ahead | MAE = 6.64---7.25% RMSE = 12.47---13.3% \( r\left(\rho, \overline{\rho}\right) \)= 85.81---87.65% | − Accuracy of model strictly related to accuracy of historical database (Achieved reasonable accuracy) |