## 3 Results and Discussion
### Forecast of Solar Radiation  
In order to predict solar radiation in tenths of kiljoules per square meter (kJ/m\({}^{2}\)), the input data used were solar radiation in tenths of kJ/m\({}^{2}\). The optimal size of the sliding window is 1. The system was trained using data of 2005 and testing with the remaining years. Best results are shown in Table 9 and Figure 11.  
The best classification system to use to predict solar radiation are those based on SVM with linear or polynomial kernels. Figure 10: Wind speed forecasting in GC for the year 2004, using SVM polynomial (input: wind speed in km/h). \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**MSE in Wind Speed Forecasting**} \\ \hline \multirow{2}{*}{**Method**} & **MSE Average (2003–2007)** & **MSE Average (2003–2007)** \\  & **in South Tenerife** & **in Gran Canaria** \\ \hline \multicolumn{3}{c}{**SVM Linear**} & 1.038 & 0.615 \\ \multicolumn{3}{c}{**SVM RBF**} \\ \(\gamma=1.54\times 10^{-4}\) & 1.269 & 0.782 \\ \multicolumn{3}{c}{**SVM Polynomial**} \\ \(\gamma=6\times 10^{-5};d=5;r=5\) & 0.974 & 0.566 \\ \multicolumn{3}{c}{**Regression tree**} \\ \multicolumn{3}{c}{**Fit Linear Model**} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Forecasting of wind speed (km/h), using wind speed (km/h) as input data. The best system to predict precipitation varies depending on the location. In South Tenerife best results are achieved using a setting based on fit linear model system, while for Gran Canaria, SVM with polynomial kernel was used. This is another innovation of the system, allowing to obtain forecasting of precipitation in a specific location despite of the difficulty of the amount of information needed to make it, by adapting the optimal size of the sliding window. The MSE averages of the best systems (higher accuracy) in all cases are represented in Table 11, high variability data offers more difficulty to obtain high accuracy forecasting (e.g., wind and solar radiation). Once the test has been conducted, it proceeds to discuss which system is best for predicting each weather parameter and differences between error rates depending on location. In addition, a comparison is also made with results obtained in other works. Generally speaking, best results are obtained using systems based on SVM with a polynomial kernel most of the time. However, with the other systems are also achieved good results, as observed in the figures and tables below. Only for comparison purposes, using the same database (AEMET), authors have made a test with a system based on ANNs, better results are also achieved with the system used in this research, as shown in Table 12. The characteristics of the ANN used in the tests are back-propagation algorithm, 24 neurons on the hidden layer and one neuron on the output layer; more details available in [16; 26]. Table 13 introduces best results obtained. If temperature is the weather parameter to predict (see Table 13), the best solution is to use temperature in \({}^{\circ}\)C and temperature in tenths of \({}^{\circ}\)C as input parameters. As regressive function to obtain a predictive model, the best option is to use SVM with a polynomial function kernel. The reason why worst results are obtained in TF is because the variation between consecutive samples is higher in TF than GC. However, good results are achieved in both cases. In case of predicting wind speed (see Table 13), using as input parameters the previous values of wind speed in km/h, the best system is SVM with polynomial kernel function. The reason for \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Place**} & \multicolumn{4}{c}{**Best MSE Average in Forecast of:**} \\ \cline{2-5}  & **Temperature (\({}^{\circ}\)C)** & **Wind (km/h)** & **Solar Radiation (kJ/m\({}^{2}\))** & **Precipitation (mm)** \\ \hline
**Gran Canaria** & 0.073 & 0.566 & 7.820 & 0.151 \\
**Tenerife** & 0.136 & 0.974 & 7.451 & 0.119 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Best MSE average per weather parameter.