# Evaluation of Artificial Intelligence-Based Models for Classifying Defective Photovoltaic Cells
## 4 Conclusions  
The work presents different solar _PV_ cell defect classifiers, using five different classifier models, _KNN_, _SVM_, _RF_, _MLP_, and _CNN_. The classification was carried out based on EL images and I-V curves, all of them at the solar _PV_ cell level. For all cases, good classification was obtained, and the differences between the proposed models were analyzed. _CNN_ presented the highest percentage of success, with 81.6%, but it was also extremely slow (around 100 h). _SVM_ was the model that presented the second-highest efficiency (76.07%), and it was the model with a reasonably short computation time. The classifiers' biggest application is in defective solar _PV_ cells classification. Furthermore, this group of cells is the one of greatest interest since it is the group that contains cells with almost zero electrical production. The work also presents a method to be used to select variables of interest, which will serve to train the different models of the classifiers. This process is essential since the use of variables without relevance can cause noise in training and the consequent obtaining of bad results in the classification. The study has focused on _PV_ solar cells from a single _PV_ solar module. As future work, it is proposed that the dataset should be extended, and the evaluation of how this extension improves the results in each of the five models should be evaluated. We will also work on collecting data from individual cells (isolated, which are not part of a module) to expand the dataset in the future. We are also going to explore the option of generating synthetic data with _GAN_ networks. The authors will also work on collecting data from isolated individual PV cells (which are not part of a module) to expand the dataset in the future. We are also going to explore the option of generating synthetic data with _GAN_ networks. The authors will also extend this work, applying the classification to _PV_ solar cells to other modules. Testing _IRT_ image-based classifiers and _IRT_ and _EL_ image classifiers together are also proposed as future works. This work is of interest as it is known that both techniques are complementary in certain aspects. Another application of _AI_ in which \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Success on** & **1 Missclassified** & **1 Missclassified** & **2 Missclassified** & **2 Missclassified** & **3 Missclassified** & **3 Missclassified** \\  & **Group 3** & **as 2** & **as 3** & **as 1** & **as 3** & **as 1** & **as 2** \\ \hline
**KNN** & 0.7002 & 0.1017 & 0 & 0.5985 & 0.0117 & 0.0579 & 0.2302 \\
**SVM** & 0.9055 & 0.1377 & 0.0077 & 0.7678 & 0.0142 & 0.0044 & 0.0752 \\
**RF** & 0.7746 & 0.2525 & 0 & 0.5221 & 0.0142 & 0.0173 & 0.1939 \\
**MLP** & 0.8224 & 0.2166 & 0.0018 & 0.6058 & 0.0687 & 0.0073 & 0.0998 \\
**CNN** & 0.6956 & 0.2282 & 0.0217 & 0.3586 & 0.0865 & 0.0543 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Group 3 classification results and results of misclassification between groups. authors are working is the estimation of the I-V curve from _EL_ images and _IRT_, at the level of the solar _PV_ module. Conceptualization, L.H.-C., S.G.-S., and V.A.-G.; methodology, A.P.-R., L.H.-C., S.G.-S., and V.A.-G.; software, A.P.-R. and H.F.M.-R.; validation, L.H.-C., S.G.-S., V.A.-G., and M.d.C.A.-G.; formal analysis., L.H.-C. and M.d.C.A.-G.; investigation, L.H.-C., S.G.-S., V.A.-G. and M.d.C.A.-G.; resources, A.P.-R., L.H.-C., S.G.-S., V.A.-G., and M.d.C.A.-G.; data curation, A.P.-R., S.G.-S., and H.F.M.-R.; writing--original draft preparation, A.P.-R., L.H.-C., and S.G.-S.; writing--review and editing, L.H.-C. and M.d.C.A.-G.; visualization, L.H.-C. and M.d.C.A.-G.; supervision, L.H.-C., and M.d.C.A.-G.; project administration, L.H.-C.; funding acquisition, L.H.-C., S.G.-S., and V.A.-G. All authors have read and agreed to the published version of the manuscript.