## 3 Results and Discussion
### Forecast of Solar Radiation
Figure 12: Precipitation forecasting in GC for the year 2003, using SVM polynomial (input: precipitation in mm). the different results (better in GC than in TF) is the same as before. When predicting solar radiation parameters (see Table 13) when using registered data about radiation in tenth of kJ/m\({}^{2}\) as input, the best system can vary by location. In both cases, good results are obtained. For the forecasting of precipitation (see Table 13), and looking to data presented above, the best results are achieved in TF than in GC. The reason is that it occurs more often in TF, which allows for a better forecasting model. Table 14 shows a comparison between the results obtained in this research versus previous studies, related to temperature, described in the introduction section [10; 11; 12; 15; 16]. In all cases, our approach presents an important improvement versus all works. Regarding the sampling period, samples are collected hourly in [10], there are no data about sampling frequency in [11], samples are collected every 10 min from October 2012 to February 2013 in [12], samples are collected every 30 min in [15] and the same database of this study and under the same conditions has been used in [16]. It comes from different geographical locations, from Tunisia in [10], from China in [11], from Australia in [12], from Costa Rica in [15] and from Canary Islands (Spain) in [16], thus the climate conditions has a great variation from one location to another. Our work and [19] presents the same result even with different sampling period. Under the same conditions and with the same database our work improves the results of [16], proving the goodness of the system. If sampling period could be reduced in stations used for testing this system, the approach can be improved in the future. In addition, this work can compare two geographical locations where meteorological data varies differently. In Tenerife (28\({}^{\circ}\)02\({}^{\prime}\)40\({}^{\prime\prime}\) N 16\({}^{\circ}\)34\({}^{\prime}\)21\({}^{\prime\prime}\) O), data from temperature and wind speed have more \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**MSE in Temperature**} & \multicolumn{3}{c}{**MSE in Wind Speed**} & \multicolumn{3}{c}{**MSE in Solar Radiation**} & \multicolumn{3}{c}{**MSE in Precipitation**} \\  & \multicolumn{3}{c}{**Forecasting (\({}^{\circ}\)C)**} & \multicolumn{3}{c}{**Forecasting (km/h)**} & \multicolumn{3}{c}{**Forecasting (kJ/m\({}^{2}\))**} & \multicolumn{3}{c}{**Forecasting (mm)**} \\ \hline \multirow{2}{*}{**Method**} & **South** & **Gran** & **South** & **Gran** & **South** & **Gran** & **South** & **Gran** \\  & **Tenerife** & **Canaria** & **Tenerife** & **Canaria** & **Tenerife** & **Canaria** & **Tenerife** & **Canaria** \\ \hline \multirow{2}{*}{**SVM Polynomial**} & 0.136 & 0.073 & — & 0.566 & — & 7.820 & — & — \\  & **—** & — & 0.974 & — & 7.451 & — & — & 0.151 \\ Fit Linear Model & — & — & — & — & — & — & 0.119 & — \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Summary results table. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Average Errors**} \\ \hline
**System** & **Place** & **Temperature** (\({}^{\circ}\)**C) & **Wind (km/h)** \\ \hline
**SVM Polynomial** & **Gran Canaria** & 0.073 & 0.566 \\
**Tenerife** & 0.136 & 0.974 \\ \hline
**ANN** & **Gran Canaria** & 0.410 & 0.850 \\
**Tenerife** & 0.567 & 1.020 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: SMV vs. Artificial Neural Network (ANN). variability that in Gran Canaria due to the location of the station in the south zone of the island. In Gran Canaria (27\({}^{\circ}\)55\({}^{\prime}\)55\({}^{\prime\prime}\) N 15\({}^{\circ}\)23\({}^{\prime}\)12\({}^{\prime\prime}\) O). The location is in the south east of the island in the border line of the zone under influence of the Trade Winds that causes softest conditions, as a result of this less variation in data leads to better results in temperature.