## 4 Discussion
Heavy soiling was observed during the spring of 2019 in Central Greece which resulted in an average decrease of 5.6% [27] in energy generation. This fact may be related to the observed deterioration in the models' performance for 2019. One must keep in mind that the dust and soiling effects are reversible, e.g., by the short-term accumulation and the subsequent meltdown of snow on the panels' surface. This fact may partially explain the characteristic error fluctuations from year to year in Figure 17.  
Now, degradation is an important factor that must be considered in the long-term prediction of a PV plant's performance. In the specific case study, due to the above-mentioned implications of fluctuating dust and soiling effects that overlap with the monotonous efficiency degradation, the short training period of three years may be the cause of the propagation of this instability. In order to further investigate this matter, it was decided to extend the training period of the ANN by two more years, to a total of five years. This is model version M3, which is added in Figure 17 and Table 3, for the years 2019 and 2020. Now, it is interesting to compare in Table 3 the prediction errors of this second scenario for  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**2016**} & \multicolumn{2}{c}{**2017**} & \multicolumn{2}{c}{**2018**} & \multicolumn{2}{c}{**2019**} & \multicolumn{2}{c}{**2020**} & \multicolumn{2}{c}{**2021**} \\ \hline  & **M1\({}^{\text{1}}\)** & **M2\({}^{\text{2}}\)** & **M1\({}^{\text{1}}\)** & **M2\({}^{\text{2}}\)** & **M1\({}^{\text{1}}\)** & **M2\({}^{\text{2}}\)** & **M1\({}^{\text{1}}\)** & **M2\({}^{\text{2}}\)** & **M1\({}^{\text{1}}\)** & **M2\({}^{\text{2}}\)** & **M1\({}^{\text{1}}\)** & **M2\({}^{\text{2}}\)** \\ \hline N [samples] & \multicolumn{2}{c}{14,416} & \multicolumn{2}{c}{14,107} & \multicolumn{2}{c}{14,231} & \multicolumn{2}{c}{14,312} & \multicolumn{2}{c}{14,477} & 6233 & 6233 \\ \hline RMSE [W] & 3.4 & 2.4 & 4.8 & 4.1 & 3.2 & 2.4 & 6.4 & 5.7 & 0.1 & 0.1 & 12.7 & 10.8 \\ \hline MBE [W] & 97.6 & 122.5 & 64.1 & 74.6 & 90.1 & 108.4 & 302.6 & 314.5 & 43.4 & 83.7 & \(-340.4\) & \(-270.3\) \\ \hline MAPE [\%] & \(-1.81\) & \(-22.27\) & \(-1.13\) & \(-1.32\) & \(-1.74\) & \(-2.10\) & \(-5.75\) & \(-5.97\) & \(-0.82\) & \(-1.59\) & 6.28 & 4.99 \\ \hline MAE [W] & 258.5 & 147.1 & 306.8 & 148.5 & 267.5 & 148.5 & 411.8 & 328.6 & 395.4 & 349.9 & 625.0 & 470.6 \\ \hline nRMSE [\%] & 6.6 & 4.6 & 8.8 & 7.4 & 6.2 & 4.6 & 12.5 & 11.2 & 9.7 & 9.8 & 15.9 & 13.5 \\ \hline COR & 0.992 & 0.997 & 0.984 & 0.989 & 0.993 & 0.996 & 0.977 & 0.982 & 0.981 & 0.983 & 0.969 & 0.958 \\ \hline PR & 0.87 & & 0.88 & & 0.86 the remaining years 2019 and 2020. The improvement in the MAPE and nRMSE with the 5-year training of the ANN model is also apparent in Figure 17 (years 2019-2020). Table 3 compares the performance prediction for 2019 and 2020 for the regression model and FF ANN with 3-year training, and the FF ANN with 5-year training. The ANN models' performance is improved with the extended training; however, there is not yet available a fair ground for comparison between the two ANN training scenarios: the monitoring of the PV park must be extended for at least two more years in order to assess whether the extension of the training period improves the performance for long-term analysis.