## 4 Discussion  
The need to optimize photovoltaic plants makes soiling accumulation an important variable. It has been shown to have a significant effect on performance and to cause numerous losses; for this reason, it should not go undetected. Knowing these losses provides the opportunity to propose efficient maintenance without exhausting water or unnecessary resources since the final objective is to obtain the cleanest energy possible. Performing maintenance on one panel but not another is a good way of comparing and studying any possible differences. This is what was done using an experimental photovoltaic plant located at CIESOL. Several sensors and various meteorological variables were measured in order to study their effect on soiling. In this work, only two panels were used to characterize dust losses. The panels had been positioned at 22\({}^{\circ}\), oriented to the South. Artificial neural networks were used to estimate the losses caused by soiling contamination on the photovoltaic panels. The correlation of all these variables was studied, and it was concluded that none of the variables had a strong correlation with the losses; thus, there was no linearity between the input and output variables. To construct the model, 80% of the data were used, while the remaining 20% were used for model validation. The modeling was performed using artificial neural networks, studying which variables were the best as possible inputs, as well as finding out the number of neurons with which best results were obtained. In this context, two different scenarios were defined. In the first scenario, soiling losses were estimated using the module temperature, the irradiance on the array plane, the ambient temperature, and the solar altitude. The results obtained showed that the nMBE was close to 0% whereas the nRMSE was less than 12%. In the case of the \"r\" correlation coefficient, the value corresponded to 0.77, which showed a strong correlation between the real and estimated losses. In the second scenario, the same soiling loss was modeled based on the module temperature, the irradiance at the array plane, the relative humidity, the short circuit current, and the solar altitude. The results show a very significant improvement compared to the model without Isc, obtaining an nMBE of 0% and an nRMSE value of approximately 6.80%. The \"r\" correlation coefficient had a value higher than 0.90, showing how successful the model is. It could be said that for both networks, the input variables were the same, although they replaced the ambient temperature with the relative humidity in model 2 and add the short circuit current. Therefore, it is intuitive that the variables needed to estimate these losses are the temperature in the panel, the irradiance in the plane of the array, and the solar altitude, including the short circuit current in model 2. The other variables are more stable and provide less information, and therefore they are more dispensable. If two more modern panels are used, it is likely that the power will vary, even the Isc will surely vary, but the aim is to have the ratio between the clean and the dirty panel. Regardless of the values achieved, the ratio will give a value of difference and would be similar whatever the technology unless the panels used some antisoiling coating; but in this case, the price of PV panels cannot be compared with conventional PV panels. Consequently, the novel technology presented in this work demonstrates that a satisfactory model has been developed to estimate the losses caused by soiling, a factor that is important and relevant in these systems. As a suggestion for possible future work, a study could be carried out to relate the influence of solar altitude, or time of day, with soiling losses. Also, it is expected to be able to contemplate the degradation of the panels for future studies, as well as the possible extrapolation of the method to other plants and geographies.