## 5 Results in Hybrid Techniques: Support Vector Machine with Search Algorithms Review
Figure 5: Training process using climatic variables for SVM models. In recent years, the state-of-the-art with respect to SVM has been demanding due to its high performance, so various methods of improving these models have been developed, such as integrating a search algorithm to the SVM model. Some works have proposed various hybrid techniques based on the flow diagram in the Figure 6. \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} **Kernel** \\ **Function** \\ \end{tabular} & **MAPE** & **RMSE** & **MBE** & **MAE** & **RRMSE** \\ \hline
[76] &
\begin{tabular}{c} Compared ANN and SVM \\ in predicting the solar radiation \\ \end{tabular} & 1 day & polynomial & - & 28.39 \(\frac{W}{m^{2}}\) & - & - & - \\ \hline
[83] &
\begin{tabular}{c} SVM performs better than other \\ models if air quality index is used \\ in the models using polynomial \\ kernel function \\ \end{tabular} & 1 h & Polynomial & 8.24\% & - & - & - & - \\ \hline
[36] &
\begin{tabular}{c} They compared the SVM with \\ (ANN) and Non linear \\ autoregressive (NAR), \\ showing that SVM performs \\ well in prediction accuracy \\ \end{tabular} & 1 h & RBF & - & 4.26 \(\frac{W}{m^{2}}\) & - & - & - \\ \hline
[82] &
\begin{tabular}{c} SVM performs great than ANN \\ models and can be effectively \\ used for grid operations and \\ energy management systems \\ \end{tabular} & 15 min & RBF & - & 28.00 \(\frac{W}{m^{2}}\) & - & - & - \\ \hline
[67] &
\begin{tabular}{c} Polynomial kernel function \\ performs great using \\ temperature equations \\ in SVM models \\ \end{tabular} & 1 h & Polynomial & - & 0.83 \(\frac{M}{m^{2}}\) & - & - & 9.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Techniques of solar radiation prediction using SVM. Figure 6: Flow chart of SVM model with search algorithm. The work of [14] showed an SVM with the Firefly Algorithm (FFA) to estimate future solar irradiance. A Firefly Algorithm (FFA) was used to calculate the best kernel parameters of the SVM model to obtain the most accurate prediction data. The model presents a better performance compared to ANN, which has RMSE = 1.86% and a MAPE value of 11.51%. The results are promising for the use of FFA with SVM as a great alternative to predicting the global component of the solar radiation. Additionally, they specified a risk in zones with no constant climatic conditions. In [84], a new approach was developed using data mining techniques to model and improve the prediction of hourly global solar radiation the next day. They report an RMSE value of 23.5% when the independent variables were the values of the meteorological parameters of the previous day and an RMSE of 22.9% when the independent variables were the forecasts of meteorological variables for the same day to be forecast. The authors [85] developed an SVM model optimized by glowworm swarm optimization (GSO) to estimate solar radiation. Employing the Eclat algorithm (data mining) to choose the appropriate predictors, the support vector machine (SVM) with penalty function of the model structure and the GSO algorithm to improve forecast accuracy, selecting the appropriate parameters. The tests were carried out in four areas of the USA using the input variables of average daily airmass, dew point, relative humidity, opaque cloud cover, wind speed, vertical wind shear, pressure, albedo, zenith angle, azimuth angle, net radiation, global normal radiation and global extraterrestrial radiation. The results show a great performance--RMSE = 0.43 \(\frac{\text{W}}{\text{m}^{2}}\) and MAPE 5.64%. In [86], an SVM-FFA model for the prediction of the monthly mean daily horizontal global solar radiation in the port of Bandar Abbass was proposed, located in the southern coastal region of Iran. They also show that their model is highly efficient in estimating the monthly mean daily horizontal global solar radiation. With the following statistical indicators, MAPE = 3.3252. Ref.