# Applications of Artificial Intelligence to Photovoltaic Systems: A Review
## 4 Discussion  
In Section 3, the different IA techniques applied are reviewed for each problem by explaining various important works. In this section, a discussion about the state of the art is performed, summarizing the trends of research and some possible new approaches to consider. The tracking of the Maximum Power Point has been considered in numerous ways, from traditional and simple methods to methods that use complex technology such as neural networks. The most simple methods still have importance, since a large amount of systems do not need complex MPPT algorithms in order to optimize production. The most complex algorithms are used only in the biggest power plants, where the configuration of the PV arrays, and the large amount of them, makes the process of tracking the Maximum Power Point more complex. Comparing the different techniques presented, it is clear that the most used technologies are the Feed-Forward Neural Networks and the FLC systems (Figure 8). Neural Networks perform better than Logic Systems, but they have some flaws. Neural Networks are highly demanding in terms of computational cost compared with FL systems. The need for large amounts of data is an intrinsic problem of Neural Networks, but it is not as important as it used to be thanks to the high availability of data. Another important problem is the complexity in the optimization of the hyper-parameters, since Neural Networks have a large amount of them. The solution has been found by using optimization algorithms, such as metaheuristics. These algorithms can be used to find the best combinations of parameters or even to find the optimal architecture. As we observed, the problem is still regarded nowadays, since there are ways to improve the efficiency and performance of the most complex systems. To fulfill this objective, more of the newest technologies applied in other sectors should be tried, since as it has been confirmed with the previous works that these kinds of algorithms perform really well on this problem. The FL methods and metaheuristics are usually implemented with MATLAB, but Neural Networks can be implemented with Python as well, using libraries such as Tensorflow [141]. Figure 8: Different algorithms for each of the problems. (**a**) MPPT; (**b**) forecasting; (**c**) parameter estimation; (**d**) defects detection. Forecasting is a key problem in PV systems. The estimation of the energy produced by solar plants has been approached as a regression problem in the majority of papers. Due to the availability of data, neural models are highly suitable for solving this problem (Figure 8). The trend is to use the newest neural architectures while optimizing their parameters and their architectures using other methods such as metaheuristics. As observed, an increase in the complexity of the networks improves the results, but this is not the only way of increasing performance. The combination with other systems such as Wavelet Transform increases reliability. Alternative network systems such as Recurrent Networks or Dentritic Networks further improve the results of traditional Neural Networks. This area also has some room for improvement since forecasting is a tricky problem due to its dependence on a large amount of variables. For future research, it would be interesting to test a new combination of parameters, improve the datasets or even try the most innovative technologies that have been used in similar problems. Most of the works are run on MATLAB, but there is an increase in the presence of Python in the latest years due to the Deep Learning Libraries. Estimating the parameters of the PV models has been conducted with a large number of different algorithms, most of them from the family of metaheuristics (Figure 8). The results are quite similar between them in terms of error (Figure 9); this fact shows that trying to further minimize error is not a worthy effort. The most promising works present a mixture of different metaheuristics, solving the problems or flaws of one metaheuristic by using others.