## 3 Results and Discussion
These losses are significantly affected by snow removal processes, including sliding, melting, and removal by wind. Snow losses can be neglected for the case of vertical BIPV facades. Losses due to the progressive degradation of the modules (ageing) are generally expressed as a yearly percentage that can vary over time, usually ranging between 0.5% and 0.7%, but this can increase based on the system and the location [79; 80]. The ageing loss was set to zero since the modules were new when the data were taken. No significant shading occurs in the considered case study. Only shading associated with the sun's position relative to the surface orientation and tilt angle is included in the calculations. Therefore, only the additional shading cast by obstacles is obviated, but it should be considered in each particular case. However, shading losses depend on the local topology, even for the same geographical coordinates. These losses can be analysed with Geographic Information System tools and data to provide estimations on the effect of shading on the incoming irradiance, as suggested in [81]. The proposed methodology can be refined by delving into additional \(PR\) factors such as soiling and solar spectrum effects. Regarding soiling, the more horizontal the surface of the modules, the higher the impact on the losses, and vice versa. Some attempts have led to systematic characterisation studies of soiling losses under different climates and boundary conditions, which help not only in predicting the influence of this variable in PV production but also in decision-making regarding the cleaning schedule of such PV systems [82; 83]. Soiling losses highly depend on in situ factors such as rainfall or wind, making it difficult to develop a priori estimations and meaning it is necessary to perform experimental testing for the desired tilt and azimuth of the BIPV system. Nevertheless, soiling losses can be minimised through proper maintenance of the PV modules. Spectral behaviour, which is highly dependent on the location, has been extensively addressed for decades [84; 85]. Commonly used models for estimating the spectral impact \begin{table}
\begin{tabular}{c c} \hline
**Loss Type** & **Value** \\ \hline Grid availability & 0.03 \\ Connections & 0.005 \\ LID & 0.0145 \\ Mismatch & 0.02 \\ Rating & 0.01 \\ Wiring & 0.02 \\ Snow & 0 \\ Ageing & 0 \\ Shading & 0 \\ \hline \end{tabular}
\end{table} 
Table 2: Assumed values for additional losses in \(PR\) calculations. on PV performance [86; 87; 88; 89] depend on the air mass, aerosol optical depth, and precipitable water and do not consider the different components of solar radiation separately. This makes them unsuitable for exploring the spectral influence as a function of the module's surface azimuth and tilt angles. Especially relevant is the case of vertical facades, where the spectral distribution of the incoming solar irradiance might differ notably from the standard. Specific spectral models for facades that depend on the angle of incidence would be needed to account for the spectral impact of different facade orientations. In addition, because vertical or nearly vertical modules face the ground as well as the sky, the albedo of the surrounding topology and ground surfaces may have an additional impact [90]. [PERSON] et al. [91] developed a deep learning model trained with remote sensing and ground station observations for surface albedo forecasting applications, which is particularly useful for bifacial PV modules and facade PV systems. The contribution of the spectral behaviour to the \(PR\) is difficult to generalise. On the one hand, the impact of the incident spectrum can change significantly for very high tilt angles (as in the case of facades), since the influence of the incident angle (which includes the surface orientation and Sun elevation) is remarkable; in this cases, the proposed spectral models, which depend on the air mass, are not valid to model the spectral effects.