## 3 Results
### Model 2 Results  
In the search for an optimal result to moderate these losses, the short-circuit current was incorporated into the neural network as a possible input variable. Figure 10 compares the actual and estimated soiling losses with ANN of a photovoltaic panel.  
The graph shows how the network more efficiently estimated the losses by using the short-circuit current as an extra input. The settings were now finer, and a further adjustment of the soiling accumulation trends can be seen. The soiling accumulation overtime was still visible, with continuous rises and falls, as well as two large peaks in the middle of the graph. Therefore, during this period, the soiling accumulated to a greater  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MBE (\%)** & **nMBE (\%)** & **RMSE (\%)** & **nRMSE (\%)** & **r** \\ \hline
0.00 & 0.03 & 1.60 & 11.60 & 0.77 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Validation results of the soiling losses estimation without Isc.  
Figure 10: Comparison of real versus estimated losses of photovoltaic (PV) panels with Isc.  
extent than in previous scenarios, probably as a result of suspended soiling that had finally precipitated onto the panels. Figure 11 shows the scatter plot comparing the real losses and the estimated losses with ANN.  
This was a positive correlation because it was upward and linear, with most points following the same trend. The point cloud is denser in the first part, dispersing at the higher values, showing that the most common loss values are in the range between 0% and 6% losses. The important achievement of the model is that the point cloud is very homogeneous, with a clear linear trend (although some specific cases go beyond the fit line). Table 8 presents the parameters obtained in the network validation.  
The table shows that the model was neither underestimated nor overestimated. One can also see a very significant improvement in the model error, given that the nRMSE, with a value of less than 7%, signified a 5% improvement over the previous model. Therefore, one can conclude that the best model was that using the short-circuit current of the unmaintained panel to estimate the losses caused by soiling contamination. The \"r\" correlation coefficient shows a high positive dependence between the two variables, which was excellent.  
Comparing with other models in the literature, ([PERSON] et al., 2019) ([PERSON] et al., 2019) used Iglo, wind speed and direction, Tamb, RH, and rainfall to create a neural network and see the effect of soiling in Morocco, resulting in a neural network with a total of 35 hidden layers, where the RMSE was close to 0.5%, the Mean Absolute Percentage Error (MAPE) greater than 9% and the higher r-value of 0.96. In this sense, the model presented in this work  
Figure 11: Scatter plot of actual vs. estimated losses with Isc.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MBE (\%)** & **nMBE (\%)** & **RMSE (\%)** & **nRMSE (\%)** & **r** \\ \hline
0.01 & 0.03 & 1.04 & 6.79 & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Validation results of the soiling losses estimation with Isc.  
needs two fewer variables (equivalent to a wind sensor), and the number of hidden neurons is reduced, making its execution more efficient and where the results are quite similar.