## 6 Conclusions  
This paper presents the development of four models to predict PV production, an analytical model developed in the open-source software OpenModelica and three AI models: an FFNN, a NARX, and an SVR. All four models are designed to predict the production of the next 24 h, with a time interval of 30 min. Predicting PV production is not an easy task due to the high dependence on solar irradiance. This work uses historically measured data to carry out the prediction. Firstly, one of the conclusions to be highlighted is that in the framework considered, the highest prediction accuracy is obtained with the SVR model. This model obtains an average R\({}^{2}\) of 0.934 for the July forecast. Thus, in this case, study, the SVR model performs 4.07% better than the FFNN model, 5.12% better than the NARX model, and 4.18% better than the analytical model. Regardless of the technique used, it is concluded that forecasting on sunny days performs better than forecasting on cloudy days. This is related to the fact that PV production on sunny days is more constant, giving the characteristic bell shape of an ideal PV system production. In contrast, the forecast for cloudy days needs to cope with sudden changes. With regard to the relative error committed on each day of July, this has been calculated in order to analyze in more detail, which is the behavior of each model for every day of the predicted month. The best performance is obtained with the model SVR, which in 58% of  
Figure 15: (**a**)Percentage distribution error for the month of July by the FFNN model; (**b**) Percentage distribution error for the month of July by the NARX model; (**c**) Percentage distribution error for the month of July by the SVR model; (**d**) Percentage distribution error for the month of July by the analytical model. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Error (\%)** \\ \hline FFNN & 5.87 \\ NARX & 6.35 \\ SVR & 5.16 \\ MODELICA & 9.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Mean relative error of four models. the cases it, commits an error of less than 4%. In comparison with the rest of the models, the SVR model performs 13.76%, 23.06%, and 83.3% better than the FFNN, NARX, and the analytical model, respectively. Another conclusion has been drawn from the development of the different models. In terms of the development process, the analytical model requires a higher level of knowledge about how a PV installation works. Furthermore, when implementing changes in the PV installation, such as an increase in installed capacity or the degradation of the PV panels, it is more complex to implement them in the analytical model. These changes have to be manually included so that they can represent the reality of the PV installation as accurately as possible. In contrast, for AI-based models, it would only be necessary to re-train the models with the new data. One of the changes applied in this work, compared to previous work [34], is the use of a filter that sets the solar irradiance values between sunset and sunrise to 0. Applying this step to the pre-processing and post-processing of the data has helped to improve the behavior of the four models during the night. It should also be noted that a good pre-processing of the data is as important as the design of the model itself. Regarding future work, a new comparison of the different models will be carried out considering predicted meteorological data instead of historical measured data. It can be thought that the predicted data, for instance, solar irradiance, will have much more influence on the results than, for example, the type of model used. Therefore, meteorological forecast data provided by different meteorological agencies will be considered, and their effect on the prediction will be analyzed. Finally, the best prediction model that uses the best meteorological forecast data will be implemented in the Izarbel EMS in order to operate in real time. Conceptualization, G.E., I.Z. and H.C.; Data curation, G.E., I.Z., J.H. and O.C.