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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)