## 3 Results
Airmass factor calculation should operate as an indicator in order to compare values of time series in different seasons and hours when sunbeams have the same length of trail. Next, we proceed to compare the results of the ANN and the linear regression model predictions to the actual performance during characteristic periods in the years 2016 to 2021. Figure 9a compares the results of the linear regression model and the ANN with the actual performance data during three consecutive cloudy days in 2016. As expected, the prediction with the more complex (nonlinear) curve fitting of the ANN is always closer to the actual data. The same behavior is observed during the sunny days (Figure 9b). The comparison continues with three consecutive cloudy days in 2017, followed by three sunny days (Figure 10). The performance of the ANN is more accurate in these examples from 2017. The linear regression model lacks accuracy in prediction, especially during the high insolation time during the noon hours of sunny days. Figure 6: Correlation between backsheet temperature and AC power output. Figure 7: Correlation between AC power output, irradiance, and backsheet temperature. The comparison continues in Figure 11 with three cloudy and three sunny days, with similar findings. The ANN prediction continues to be closer to the measurement, as seen by the qualitative comparison in Figure 11.  
Figure 8: Correlation between AC power output, airmass, and irradiance. Figure 10: Comparison between real power output and two models forecasting for three cloudy (**a**) and three sunny days (**b**) for 2017. Figure 3:0 Comparison between actual power output and forecasts of the two alternative methods for three consecutive cloudy (**a**) and three sunny days (**b**), respectively, from 2016. In summary, as regards the years 2016 to 2018, which are closer to the training period, the characteristic results of Figures 9-11 show that both models forecast the power output with a very good accuracy, the ANN model being generally superior. The nRMSE values fluctuated between 4.6 and 7.4% for the ANN model and 6.2-8.8% for the linear regression model. Respectively, MAPE values fluctuate between \(-2.27\) and \(-1.32\%\) for the ANN model and \(-1.81\) and \(-1.13\%\) for the linear regression model. It is important to note additionally that during these years the performance ratio (PR) was in the range of 0.86-0.88. Now, as one moves at more distance from the training period, Figure 12 shows that both models forecast the power output with a remarkably higher nRMSE error value for 2019 compared to the previous years. nRMSE values are \(11.2\%\) for the ANN model and \(12.5\%\) for the linear regression model. The respective MAPE values are \(-5.97\) for the ANN model and \(-5.75\%\) for the linear regression model. It must be noted here that this year was characterized by a very low PR of 0.84. On the other hand, both models have an increase in RMSE error and especially 9.8% and 9.7% for the ANN and regression model, respectively. Shifting attention to the year 2020 (Figure 13), the situation further deteriorates with the prediction accuracy of both models, which both overpredict the electricity output of the PV park. Obviously, this behavior must be related to the rate of efficiency deterioration of the PV panels. During the assessment of the first six years of operation of the specific PV park [47], the degradation rates of normalized efficiency for the first two AM classes, which have the highest impact on total energy production, varied between 1.28 and 6.92%. The degradation rate was variable during the years. Thus, the degradation embodied in  
Figure 3:1 Comparison between real power output and two models forecasting for three cloudy (**a**) and three sunny days (**b**) for 2018.