## 3 Results and Discussion
The conclusion is that, for the case study analysed and presumably for equivalent BIPV systems, the optimal approach for fitting parameters for the south ventilated facade of Building 42 was to combine the [PERSON] model, which only depends on irradiance and ambient temperature, with [PERSON]'s data processing strategy. This model, with a fitted \(\text{NOCT}_{\text{eff}}\) of 55.3 \({}^{\circ}\)C, was used to calculate cell temperature and estimate temperature losses in the PV system (Equation (4)). Concerning angular losses, they were calculated for any possible combination of the module's tilt angle and azimuth at different sites to cover any positioning in BIPV systems. The convenience of considering the [PERSON] IAM model and the derived expressions for each irradiance component, which are dependent on one fitting parameter, \(a_{\text{r}}\) (a function of the module's structure and materials and its soiling degree), lies in their suitability and simplicity. It is recommended to fit this parameter to each particular case by measuring the angular variation in the PV current as stated in the 61853-2 IAM testing procedure. Otherwise, \(a_{\text{r}}\) values reported in the literature for similar types of modules \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Temperature** & **Data Filtering** & & & \begin{tabular}{c} _MBE_ \\ (\({}^{\circ}\)C) \\ \end{tabular} & \
\begin{tabular}{c} _RMSE_ \\ (\({}^{\circ}\)C) \\ \end{tabular} & \(\mathbf{R^{2}}\) &
\begin{tabular}{c} _MBE_ \\ (\({}^{\circ}\)C) \\ \end{tabular} & \(\mathbf{RMSE}\) & \(\mathbf{RMSE}\) \\
**Model** & **Criteria** & **Fitted Parameters** & & & **2020 Minutely** & & **2020 Hourly** & \\ \hline Ross & \(G_{\text{POA}}>400\) W/m\({}^{2}\) & \(NOCT_{\text{eff}}=53.3\)\({}^{\circ}\)C & 0.3 & 4.1 & 0.72 & 1.2 & 2.3 & 0.97 \\ Ross & moving average & \(NOCT_{\text{eff}}=55.3\)\({}^{\circ}\)C & \(-0.6\) & 2.6 & 0.96 & \(-0.6\) & 2.2 & 0.97 \\ King & \(G_{\text{POA}}>400\) W/m\({}^{2}\) & \(b=-0.042\) & 0 & 3.8 & 0.76 & 1.2 & 2.1 & 0.98 \\ King & moving average & \(a=-3.063\) & & & & & & \\  & \(b=-0.048\) & \(-0.7\) & 2.4 & 0.97 & \(-0.7\) & 2.0 & 0.98 \\ Faiman & \(G_{\text{POA}}>400\) W/m\({}^{2}\) & \(u_{0}=22.75\) & & & & & & \\  & \(u_{1}=1.20\) & 0.4 & 3.8 & 0.76 & 1.3 & 2.2 & 0.97 \\ Faiman & moving average & \(u_{0}=21.35\) & \(-0.7\) & 2.4 & 0.97 & \(-0.7\) & 2.0 & 0.98 \\ VPsyst & \(G_{\text{POA}}>400\) W/m\({}^{2}\) & \(u_{0}=17.61\) & 0.4 & 3.8 & 0.76 & 1.3 & 2.2 & 0.97 \\  & \(u_{1}=0.93\) & & & & & & & \\ PVsyst & moving average & \(u_{0}=16.33\) & \(-0.7\) & 2.4 & 0.97 & \(-0.7\) & 2.0 & 0.98 \\  & \(u_{1}=0.86\) & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The application of temperature models to Building 42’s south ventilated facade, including the experimentally fitted parameters and the statistical error analysis. can be adopted. Based on the available literature, this paper assumed an \(a_{\text{r}}\) value of 0.2. In order to assess the effect of \(a_{\text{r}}\) values on angular losses, a sensitivity comparison was developed, where a variation in \(a_{\text{r}}\) of the order of \(\pm\)0.05 (e.g., to account for other angular responses) caused an impact of \(\pm\)1.1% for the total \(PR\) of the system. Additional losses are caused by modules' mismatch, wiring, connections, light-induced degradation (LID), nameplate rating, ageing, and grid availability. Characteristic values for these losses can be obtained from the NREL's PVWatts manual [46], which adapted the system losses obtained by [PERSON] et al. [74] into more realistic values by attending to the current inverter efficiency values. Table 2 summarises the selected tabulated values for the \(PR\) calculations. Efforts have been made to estimate snow losses [75; 76; 77]. System Advisor Model software [78] includes a model based on [PERSON] et al. 's model [77], which requires modules' tilt angle and local data such as daily snow depth measurements, hourly plane-of-array irradiance, and temperature values.