## 3 Results
### Meteorological and Experimental Data  
Validation of different models was performed based on meteorological and experimental data under outdoor conditions for 15 days in March. The three most common types of weather conditions were selected for comparison, namely: clear (2 nd and 11 th day), overcast (4 th and 6 th day) and cloudy (remaining 11 days). In fact, clear days are quite rare in Slovenia for the aforementioned month, so cloudy days are the most prevalent, which can be seen from the measurements of solar radiation shown in Figure 2. Furthermore, the wind speed in March is relatively low as well, with an average velocity of 1.55 m/s and a maximum velocity of 8.7 m/s. \begin{table}
\begin{tabular}{c c c c c} \hline  & \(\rho\) [kg/m\({}^{3}\)] & \(C\) [J/kgK] & \(k\) [W/mK] & \(d\) [mm] \\ \hline Glass & 3000 & 500 & 1.8 & 4 \\ EVA & 960 & 2090 & 0.35 & 0.4 \\ PV cell & 2330 & 677 & 148 & 0.3 \\ Tedlar (PVF) & 1200 & 1250 & 0.2 & 0.4 \\ \hline \end{tabular}
\end{table} 
Table 3: Thermal and mechanical parameters of the considered mono-crystalline PV module. Figure 3a presents the dynamic thermo-electric model created in Matlab/Simulink 2020b software by applying the s-function block diagram used for differential equations, while Figure 3b presents a static thermal model created in Ansys Transient Thermal 2020 R2 software for FEM analysis. The input parameters of the dynamic-thermal model made in Matlab/Simulink were solar radiation \(G\), ambient temperature \(T_{\mathrm{a}}\), wind speed \(v\), output power \(P_{\mathrm{DC}}\) and, fill factor \(FF\), while the parameters for the dynamic electric model were solar radiation \(G\) and operating temperature of the PV module \(T\). On the other hand, the parameters of the static-thermal model made in Ansys Transient Thermal were solar radiation \(G\), ambient temperature \(T_{\mathrm{a}}\) and, convective heat transfer coefficient \(h_{\mathrm{conv}}\). The validation of the existing dynamic model [10] with the static model was performed, as well. The results of both models are shown in Figures 4 and 5. Figure 3: A thermo-electric model: (**a**) Dynamic—Matlab/Simulink; (**b**) Static—Ansys Transient Thermal. Figure 2: Meteorological and experimental data of the density of solar radiation \(G\), wind speed \(v\), ambient temperature \(T_{\mathrm{a}}\), current \(I_{\mathrm{DC}}\) and voltage \(V_{\mathrm{DC}}\). The largest temperature deviations occur more or less on days with a higher proportion of direct solar radiation (clear days). On average, the temperature error for 15 days is 0.32 \({}^{\circ}\)C (dynamic-thermal) and 0.63 \({}^{\circ}\)C (static-thermal). Figure 6 shows the RMSE and MAE values obtained for different weather conditions: clear, cloudy and overcast. Figure 4: Validation of static thermal model (Ansys Transient Thermal) with measurements (for 15 days in March 2021). Figure 5: Validation of dynamic thermal model (Matlab/Simulink) with measurements (for 15 days in March 2021). It is clear that there are smaller temperature deviations in the case of the dynamic thermal model, as it is shown by the dynamic responses. Similar results are indicated by other authors [10,21,22]. It can be observed that on overcast days, there is a very small error of RMSE and MAE, due to smaller influence of direct solar radiation. In this case, the model is especially influenced by the ambient temperature \(T_{\text{a}}\) and the wind speed \(v\). Therefore, it was found that the pyranometers mounted on the dual-axis PV tracking systems measure the solar radiation with a significant error. The measurement error occurs due to non-compliance with the proposed calibration time by the manufacturer, which is also shown in Table 1 (calibration time). Temperature deviations are also affected by the missing diffuse component of solar radiation, which is not measured by pyranometers described in this study. Another important reason for the solar radiation measurement errors is dirt [45].