## 4 Conclusions  
As the temperature has a significant effect on the electrical efficiency of solar cells, it is necessary to accurately estimate the photovoltaic module/cell temperature when predicting the energy yield. In this framework, we proposed two new models in order to calculate the PV module temperature. One model with the wind was developed based on the Sandia model by considering the effect of the thermal inertia of the module, whilst the other was determined without taking into account the wind speed. These proposed models have been compared to other models in the literature by using the statistical coefficients \(R^{2}\) and \(RMSE\). The results show that the inclusion of the thermal inertia of the PV module and the wind speed with the given models allow making a more accurate estimation of the module temperature, with \(R^{2}\) correlation above 95% and the \(RMSE\) below 3 \({}^{\circ}\)C for most PV technologies. As they do not need many input parameters and are simple to implement, these proposed models have demonstrated that they also work well under different environmental and operating conditions and PV technologies. The proposed model with wind, Equation (13), can be used to calculate PV module temperature with high precision for any location where wind data are available; otherwise, Equation (14) can be applied. These thermal models are useful for evaluating the thermal performance of a PV module under different environmental and operating conditions since they have proven their reliability when being examined in different locations and climates. When climatological information such as wind speed and solar illumination are available in a time series for a certain location, our models make it possible to estimate the module temperature for different types of solar cells. The temperature information is essential in estimating the PV module efficiency and in deciding which kind of PV panel will be most economical for the given location. Conceptualization, D.P.N.N. and J.L. ; Data curation, D.P.N.N. ; Formal analysis, D.P.N.N. ; Funding acquisition, J.L. ; Investigation, D.P.N.N. ; Methodology, D.P.N.N., K.N. and J.L. ; Project administration, J.L. ; Resources, K.N. and J.L. ; Software, D.P.N.N. ; Supervision, K.N. and J.L. ; Validation, K.N. and J.L. ; Writing--original draft, D.P.N.N. ; Writing--review and editing, K.N. and J.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**PV**} & \multicolumn{6}{c}{**Implementing Results**} \\ \cline{2-13}  & \multicolumn{3}{c}{**Cocoa**} & \multicolumn{3}{c}{**Golden**} & \multicolumn{3}{c}{**Eugenea**} \\ \cline{2-13}  & \(\mathbf{\Delta T_{ref}^{*}(^{\circ}\text{C})}\) & \(\mathbf{W_{ref}}\) & \(\mathbf{R_{2}}\) & \(\mathbf{RMSE}\) & \(\mathbf{\Delta T_{ref}^{*}(^{\circ}\text{C})}\) & \(\mathbf{W_{ref}}\) & \(\mathbf{R_{2}}\) & \(\mathbf{RMSE}\) & \(\mathbf{\Delta T_{ref}^{*}(^{\circ}\text{C})}\) & \(\mathbf{W_{ref}}\) & \(\mathbf{R_{2}}\) & \(\mathbf{RMSE}\) \\  & **(m/s)** & **(\%)** & **(\({}^{\circ}\text{C}\))** & **(\({}^{\circ}\text{C}\))** & **(m/s)** & **(\%)** & **(\%)** & **(\({}^{\circ}\text{C}\))** & **(\%)** & **(m/s)** & **(\%)** & **(\({}^{\circ}\text{C}\))** \\ \hline xSi & 27.3 & 21.7 & 96.2 & 2.00 & 29.9 & 12.2 & 97.0 & 2.86 & 29.6 & 8.3 & 97.4 & 2.04 \\ mSi & 25.7 & 18.5 & 95.5 & 2.05 & 29.6 & 18.9 & 97.3 & 2.54 & 27.3 & 9.7 & 97.7 & 1.86 \\ CdTe & 30.8 & 17.5 & 94.9 & 2.47 & 31.7 & 15.2 & 97.0 & 2.54 & 30.8 & 8.5 & 96.9 & 2.37 \\ CIGS & 29.9 & 20.8 & 95.2 & 2.37 & 31.4 & 10.1 & 96.3 & 2.93 & 30.2 & 8.3 & 96.4 & 2.41 \\ HIT & 27.3 & 22.2 & 95.8 & 2.10 & 29.6 & 21.3 & 96.5 & 3.02 & 27.9 & 8.3 & 97.5 & 2.02 \\ aSiMicro & 27.1 & 19.6 & 95.8 & 2.06 & 29.6 & 18.9 & 96.6 & 2.90 & 29.0 & 8.5 & 97.1 & 2.19 \\ aSiTandem & 31.1 & 22.2 & 95.9 & 2.27 & 29.6 & 19.2 & 96.5 & 2.91 & 31.7 & 8.8 & 97.0 & 2.38 \\ \hline \hline \end{tabular}
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