## 5 Results in Hybrid Techniques: Support Vector Machine with Search Algorithms Review
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Reference** & **Analysis of Results** & \begin{tabular}{c} **Time** \\ **Horizon** \\ \end{tabular} & \
\begin{tabular}{c} **Optimization** \\ **Model** \\ \end{tabular} &
\begin{tabular}{c} **Kernel** \\ **Function** \\ \end{tabular} & **MAPE** & **RMSE** & **MAE** & **RRMSE** \\ \hline \multirow{3}{*}{[14]} & SVM-FFA present a better & \multirow{3}{*}{1 h} & \multirow{3}{*}{FFA} & \multirow{3}{*}{RBF} & \multirow{3}{*}{11.51\%} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{1.86\%} \\  & performance in comparisson & & & & & & & \\  & with ANN models & & & & & & & \\ \hline \multirow{3}{*}{[84]} & Mining data to forecasting & \multirow{3}{*}{1 h} & \multirow{3}{*}{SVM-R} & \multirow{3}{*}{Sigmoid} & \multirow{3}{*}{-} & \multirow{3}{*}{119 \(\frac{\text{W}}{\text{m}^{2}}\)} & \multirow{3}{*}{79 \(\frac{\text{W}}{\text{m}^{2}}\)} & \multirow{3}{*}{22.90\%} \\  & hourly global solar radiation & & & & & & & \\ \hline \multirow{3}{*}{[85]} & Evaluated the performances & \multirow{3}{*}{1 day} & \multirow{3}{*}{GSO} & \multirow{3}{*}{Hilbert space} & \multirow{3}{*}{5.64\%} & \multirow{3}{*}{0.43 \(\frac{\text{W}}{\text{m}^{2}}\)} & \multirow{3}{*}{-} & \multirow{3}{*}{-} \\  & of SVM, HARD-RIDGE-SVM, & & & & & & & \\  & SVM-HARD and & & & & & & & \\  & GSO-SVM-HARD model & & & & & & & \\ \hline \multirow{3}{*}{[86]} & SVM-FFA present a better & \multirow{3}{*}{1 month} & \multirow{3}{*}{FFA} & \multirow{3}{*}{RBF} & \multirow{3}{*}{3.32\%} & \multirow{3}{*}{0.18 \(\frac{\text{kW}}{\text{m}^{2}}\)} & \multirow{3}{*}{-} & \multirow{3}{*}{3.73\%} \\  & performance compared to & & & & & & & \\ \cline{1-1}  & the ANN, GP, and & & & & & & & \\ \cline{1-1}  & ARMA techniques & & & & & & & \\ \hline \multirow{3}{*}{[87]} & SVM parameter optimization & \multirow{3}{*}{-} & \multirow{3}{*}{GA} & RBF & \multirow{3}{*}{8.24\%} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} \\  & using GA is more than 15.9 times & & & & & & & \\ \cline{1-1}  & faster than using grid search. & \multirow{3}{*}{1 day} & \multirow{3}{*}{Wavelet} & \multirow{3}{*}{RBF} & \multirow{3}{*}{4.69\%} & \multirow{3}{*}{1.18 \(\frac{\text{M}}{\text{m}^{2}}\)} & \multirow{3}{*}{0.92 \(\frac{\text{M}}{\text{m}^{2}}\)} & \multirow{3}{*}{5.94\%} \\  & F-measure was used to & & & & & & & \\ \cline{1-1}  & evaluate the performance & & & & & & & \\ \hline \multirow{3}{*}{[55]} & SVM incorporates a discrete & \multirow{3}{*}{1 day} & \multirow{3}{*}{Wavelet} & \multirow{3}{*}{RBF} & \multirow{3}{*}{4.69\%} & \multirow{3}{*}{1.18 \(\frac{\text{M}}{\text{m}^{2}}\)} & \multirow{3}{*}{0.92 \(\frac{\text{M}}{\text{m}^{2}}\)} & \multirow{3}{*}{5.94\%} \\  & wavelet transformation algorithm & & & & & & & \\ \cline{1-1}  & for pre-processing of inputs & & & & & & & \\ \hline \multirow{3}{*}{[57]} & SVM-GA models has higher & \multirow{3}{*}{10 min} & \multirow{3}{*}{GA} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{2.57 \(\frac{\text{M}}{\text{m}^{2}}\)} & \multirow{3}{*}{1.97 \(\frac{\text{M}}{\text{m}^{2}}\)} & \multirow{3}{*}{-} \\  & prediction accuracy in tropical & & & & & & & \\  warm sub-humiditham ANN model & & & & & & & \\ \hline \multirow{3}{*}{[88]} & GA-SVM outperform SVM & \multirow{3}{*}{1 h} & \multirow{3}{*}{GA} & \multirow{3}{*}{Gaussian} & \multirow{3}{*}{1.70\%} & \multirow{3}{*}{11.22 \(\frac{\text{W}}{\text{m}^{2}}\)} & \multirow{3}{*}{-} & \multirow{3}{*}{-} \\  & classical models in & & & & & & & \\ \cline{1-1}  & classification climatic data & & & & & & & \\ \hline \multirow{3}{*}{[89]} & SVM-FFA better than & \multirow{3}{*}{1 day} & \multirow{3}{*}{-} & RBF & \multirow{3}{*}{-} & \multirow{3}{*}{1.18 \(\frac{\text{M}}{\text{m}^{2}}\)} & \multirow{3}{*}{-} & \multirow{3}{*}{18.00\%} \\  & copula-base nonlinear & & & & & & & \\ \cline{1-1}  & quantile regression & & & & & & & \\ \hline \multirow{3}{*}{[90]} & Mentioned that SVM & \multirow{3}{*}{1 h} & \multirow{3}{*}{PSO} & RBF & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{0.99 \(\frac{\text{M}}{\text{m}^{2}}\)} & \multirow{3}{*}{2.90\%} \\ \cline{1-1}  & & with PSO convergence to & & & & & & \\ \cline{1-1}  & local optimal solution & & & & & & \\ \cline{1-1}  & faster than the other & & & & & & \\ \cline{1-1}  & proposal algorithms. & & & & & & & \\ \hline \hline \end{tabular}
\end{table}