## 1 Introduction
An example is the study of the performance ratio of residential PV systems in Belgium and France, with annual values of 0.78 and 0.76, respectively [16; 17]. The effect of electrical losses on \(PR\) was studied regarding inverter and microinverter setups and tilt and orientation angles in 200 installations in France, with an average value of 0.79 [18]. A 2014 IEA report analysed the long-term performance ratio of PV systems, showing an average \(PR\) of 0.78 \(\pm\) 0.14 [19]. \(PR\) analysis at several Indian sites was performed for free-standing PV and BIPV systems by modelling output power with PVGIS for specific tilts and orientations; \(PR\) estimations ranged between 0.68 and 0.79 [20]. [PERSON] et al. included an evaluation of the \(PR\) in PV plants worldwide from 1994 to 2022, with the \(PR\) showing a tendency to improve as years go by [21]. Several other works have used simulation tools such as PVWatts, PVGIS, PVSyst, and PV*SOL for the performance ratio determination of BIPV or BAPV systems with different tilt angles and PV technologies, some of them comparing the results with experimental data [22; 23; 24], revealing significant performance differences depending on the software tool selection [25]. However, none of these studies analysed the modelling of loss factors. Besides the \(PR\) defined in the IEC 61724-1 Standard [14], which has an experimental approach, an additional interest could be developing an analytical approach to estimate \(PR\) in advance for reliably predicting the output energy of a BIPV system during the design and sizing stages [26; 27]. Following this strategy, the present paper takes a different approach to \(PR\) by estimating system losses, primarily in terms of temperature and angular reflection impacts, from different available models to facilitate the modelling of BIPV systems. Known causes of power losses include module temperature, the effect of the angle of incidence (AOI) of irradiance on the module's reflection, solar spectrum, soiling, shading, snow, mismatch, wiring, connections, module light-induced degradation (LID), module ageing, and grid availability [28]. These losses are mostly caused by factors external to the PV system (e.g., ambient temperature, solar irradiance), although some contributions come from intrinsic factors and could be minimised through proper design, maintenance, and management. The effect of shading on the final system's performance depends on local conditions, especially the topology of the surrounding elements. Different methods have been proposed to compute the impact of shading, usually requiring a 3D model of the urban elements in the domain [27; 29; 30]. Geographic Information Systems (GISs) are frequently used to evaluate the solar potential on rooftops and building facades using 3D models to compute both the shading and the sky view factor [31; 32]. However, this approach requires both information about the performance ratio and the solar irradiation under an urban complex topology to compute solar potential and PV generation [33; 34; 35]. Over the last few decades, research supported by experimental campaigns has progressed in power loss factor modelling. In particular, module temperature [12; 36; 37; 38] and angle of incidence [39; 40; 41] performance analysis have been the subject of different studies [42]. Regarding temperature, BIPV designers do not have access to easy-to-use tools that help them determine how design decisions may affect such losses. Concerning optical losses related to the AOI (angular losses), they have not yet been systematically analysed for different BIPV applications and all usable building surfaces. Both temperature and angular losses can become critical for BIPV modules due to their structure, their mounting support, and the operating boundary conditions they are subjected to when integrated into a building [43].