## 3 Results
### DC Power  
Figure 8 shows one year of modeled versus measured DC string power of four PV configurations as simulated by six software. Good correlation is observed in all 24 regressions (R\({}^{2}\) = 0.99), but residual errors can exceed 5 kW (20%) in some cases. When such large errors are observed, the error is similar for all six software, which indicates an unidentified issue with the meteorological measurements and/or the electrical monitoring system. The DC electrical monitoring system measures string-level voltage and current independently of the inverter. The galvanically isolated data acquisition boards are a commercially available string.bloxx solution from Gantner Instruments (Rodgau, Germany). From the specifications, we determined the uncertainty of the DC power measurements as \(\pm\)0.5% at the full scale. The dashed black lines in Figure 8 are drawn at \(\pm\)4.5% from the solid black unity line and depict the GHI measurement uncertainty on a clear day at solar noon (\(\pm\)4%) and the uncertainty of the power measurements (\(\pm\)0.5%). All 24 trend lines in Figure 8 are within this boundary at measured power levels greater than 7 kW. Figure 9 shows the model errors from Figure 8 in the form of cumulative distribution functions (CDFs). The slopes of all 24 CDFs in Figure 9 are steepest around approximately \(\pm\)500 W, which indicates that the majority of errors are within this range. CDF shifts in the positive X-axis direction indicate that a modeling tool has a tendency toward higher DC power predictions, while shifts in the negative x-axis direction indicate a tendency toward more conservative DC power predictions. With this in mind, the bifacial fixed-tilt and HSAT simulations reveal two groups: PlantPredict, PVsyst, and SAM showing nearly identical CDFs; and MoBiDiG RT, MoBiDiG VF, and SolarFarmer showing very similar  
Figure 8: Regressions of hourly modeled versus hourly measured DC power of four the four PV system types studied. Result from six different software are shown. Data points recorded when the sun elevation was less than 5\({}^{\circ}\) above the horizon were removed from these plots. CDFs. The former group has a tendency toward negative bias and the latter group a tendency toward positive bias. In the case of the bifacial HSAT simulation, the reason for this two-group split could be attributed to the latter group yielding the highest estimates of \(G_{POA,Rear}\) (Figure 6). In the case of the bifacial fixed-tilt simulation--and both monofacial simulations--the explanation is not so clear and therefore likely not attributable to a single difference in submodeling steps but rather due to the differences accumulated in multiple submodels. We performed regressions of the DC power residuals as a function of measured variables, such as diffuse light fraction (_DHI/GHI_), sun position (zenith and azimuth), and \(G_{POA,Front}\). This exercise revealed no significant correlations (R\({}^{2}<\) 0.25) or systematic trends, other than a trend of larger absolute errors during times of high solar irradiance. Figure 10 shows two variability plots that summarize the annual mean absolute errors (MAEs) and mean bias errors (MBEs) of the four PV system types simulated by six software. The dashed orange lines in each variability plot represent the average modeling error of the bifacial and monofacial system types and indicate a 30 W higher MAE in bifacial simulations. The slightly higher MAE can be explained simply by the fact that the bifacial arrays produce about 5% more energy on an annual basis than the monofacial arrays. When the MAE is normalized to the average power produced by each system type over the year, we found that the normalized error is about 0.25% higher for bifacial simulations versus monofacial simulations. This difference is likely driven by the contribution of \(G_{POA,Rear}\). In terms of the average MBE, bifacial simulations are about 90 W higher than monofacial simulations but closer to zero bias than the monofacial simulations.