## 4 Discussion  
The application of linear regression (M1) and ANN (M2) provides insight into the effectiveness of these models in understanding and predicting the operation of a PV plant over the period of a decade. It is clear that the ANN model achieves better statistical metrics than the regression model, as expected by the published experience of other researchers. However, as one moves further away from the training period, both models show a significant differentiation in performance, especially during the years 2019-2021. This \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **2016** & **2017** & **2018** & **2019** & **2020** & **2021** \\ \hline Average \(\eta\) [\%] & 13.93 & 13.99 & 13.99 & 13.47 & 13.97 & 15.17 \\ Standard deviation [\%] & 1.04 & 1.51 & 1.21 & 1.56 & 1.36 & 1.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical and energy metrics. Figure 14: Comparison between real power output and two models forecasting for three cloudy (**a**) and three sunny days (**b**) for 2021. Figure 13: Comparison between real power output and two models forecasting for three cloudy (**a**) and three sunny days (**b**) for 2020.  
fact indicates a possible drop in the PV plant's performance that may be explained either by an expected degree of the degradation rate of the PV panel or by the soiling effect as there exists no systematic cleaning procedure in the plant. Another explanation could point to an error in the irradiance measurement system. The irradiance sensor described in Appendix A is by itself a reference PV cell, thus subjected to soiling with an adverse effect on the irradiance values recorded. Energy generation metrics in the form of PR and Total PV performance, indicate a decreasing trend from 2016 to 2019 that is coincident with the manufacturer's warranties. On the contrary, PR and total PV performance show an increase during 2020-2021. Moreover, the average performance for the year 2021 is significantly higher than the years 2016-2018. This fact points to an erroneous irradiance sensor. Figure 15 points to this fact by the comparison of irradiance-airmass correlations of the years 2013 and 2021. The readings for 2021 indicate a systematic shift to lower values of insolation producing useful electricity in all the airmass ranging from 1-10. This refers to a drift of sensor output. A closer look in Figure 16 reveals the problem with the soiled irradiance sensor. More specifically, the day of 26 April is a clear sky, sunny day; however, the sensor does not read correctly the maximum irradiance value of about 1000 W/m\({}^{2}\). The same observation can be carried out for the next three days, which are days with intermittent clouds; however, the maximum irradiance in the sunny intervals is always recorded at reduced levels. Table 2 summarizes the calculated statistical and energy metrics of models M1 (regression model) and M2 (FF ANN trained with three-year data), for the years 2016 to 2021. Figure 17 shows the trend of MAPE and nRMSE of the two models' predictions with time. In general, an increase in error is observed over the years. However, this increase is not monotonous, as also observed in Table 2. Of course, one cannot expect the specific approach to be very accurate for long-term forecasting, because of the erratic nature of the efficiency degradation rate of an ensemble consisting of a large number of PV panels, which are not regularly cleaned from dust and soiling. However, it is employed here as a useful tool for fault detection and degradation analysis. Previous studies, which investigate the efficiency degradation of PV plants and especially in Greece, point to an average annual degradation rate in the range of 1-4% [47]. These findings converge--on average--with the general trends resulting from the proposed method (Figure 17). Furthermore, the effect of heavy soiling is another important factor that may decrease energy production by up  
Figure 15: Comparison of measured Irradiance of 2013 and 2021 in correlation with airmass. to 6.5% on an annual basis [49].