## 3 Results and Discussion  
Regarding temperature losses, all temperature models include one or more parameters that need to be empirically fitted to each PV system's data so that the model's formula can be applied to calculate cell or module temperature properly. Ideally, these parameters should be fitted using experimental data measured in situ for each specific PV system. As Building 42 at CIEMAT has been monitored since 2017 its renovation, experimental data are available on a minutely basis, which can be used to adjust the parameters for all temperature models. When using experimental data, dynamic conditions involve sudden changes in weather variables, such as clouds or rain, which can lead to irradiance peaks and fluctuations. In addition, low wind speed and gusts can also affect the representativeness of the operating conditions. These issues have been addressed in regulations such as IEC 61853-2 [59] by using filters to reject undesired data (e.g., irradiance below 400 W/m\({}^{2}\)) [68]. A promising approach to addressing data management was recently developed by [PERSON] [70], who suggests pre-processing field measurements by delaying and smoothing their fluctuations through a low-pass filter or a moving average filter based on the effect of the thermal mass of the module on module temperature. This procedure uses rolling window calculations to obtain the average data value over several timeframes measured in minutes (e.g., 0-25 min) and performs linear regression analysis to temperature models to find the best-fitting solution, leading to a certain time in minutes to offset and smooth the data curve. Figure 3 shows the effect of this filtering technique applied to local plane-of-array irradiance data, where the delaying value was calculated to be ten minutes. The filtering strategy is applied to irradiance, temperature, and wind speed data, which are then used to fit the parameters of the desired temperature model. Table 1 summarises the results of applying regression to fit all temperature models to the experimental data. Two data processing strategies were used: filtering data with G\({}_{\text{POA}}\) above 400 W/m\({}^{2}\), as indicated in the IEC 61853-2 [59] and prior standards, and [PERSON]'s  
Figure 3: Comparison of measured and optimised (moving average) irradiance for 5 June 2020.  
optimised moving average. Parameters were obtained using minutely data for 2020 and then applied to the temperature models using minutely and hourly data, which were in turn compared to experimental data through mean bias error (_MBE_), root-mean squared error (_RMSE_), and the coefficient of determination (_R_\({}^{2}\)). From Table 1, it can be concluded that all temperature models predict module temperature with a similar degree of accuracy when applying the same data filtering criteria, regardless of the complexity of the model. This may be due to the limited effect of wind on the BIPV facade analysed in this paper (a ventilated facade, three stories high, with limited rear-side ventilation). Although wind speed may be crucial in PV plants, where modules have their backsides exposed to the air, its effect on certain BIPV configurations could be minimal, reducing the need to include it in the calculations. Additionally, the moving average strategy proposed by [PERSON] had a significantly positive impact when using minutely data for all models, consistently reducing the error and improving the module temperature prediction when compared to \(G_{\text{POA}}\) above a 400 W/m\({}^{2}\) threshold filter, with lower improvement due to [PERSON]'s approach when using hourly data. The main advantage of the moving average method is the possibility of using highly detailed data and minute-based intervals instead of hourly ones.