# Applications of Artificial Intelligence to Photovoltaic Systems: A Review
## 3 Artificial Intelligence Applied to PV Systems
### Maximum Power Point Tracking (MMPT)
#### 3.2.3 Neural Networks  
Neural Networks have shown excellent adequacy and high capabilities for complex learning problems and, thus, they are ideal for tracking the Max Power Point. They can be used alone or helped by other methods. The hybrid techniques are usually focused on improving the performance of the neural networks by optimizing the hyper-parameters of the networks since it is a really complex task. In [65], the proposed NN was composed of a single hidden layer of twenty nodes, two inputs and one or two outputs, all of them using tangent sigmoid. Two networks are built, one for approximating the voltage and current curves and the second for estimating the optimal voltage factors. The input of both networks is temperature modules and solar irradiation. The outputs for the first networks are the optimal PV voltage and optimal PV and optimal voltage factor for the second. This method improves the deficiency of traditional algorithms and improves its results. The work in [49] presents an NN composed of 5 nodes on a single hidden layer but with the novelty of prepossessing of the data via genetic algorithms, using the same inputs (irradiance and temperature) but with only one output: the Voltage at V\({}_{MPP}\). The model improves the transitional state and reduces the oscillations in the steady state compared with traditional methods. The approach presented by [66] uses a hyperbolic activation function. The structure is defined by 4 inputs, 1 output and 3 hidden layers with 8,7,7 neurons, respectively. The inputs are composed of three irradiation levels and the temperature. The output is a prediction of the PV voltage corresponding to the MPP; this output goes to a calculation block where it is converted for the traditional P&O algorithm. The training was carried out with a Bayesian regulated back-propagation, which performed better than standard BP. The results provide better efficiency compared with classical methods, even under partial shading. The authors used MATLAB for the implementation and simulations. The technique presented in [67] combines Fuzzy Logic and Neural Networks, building the system known as the Adaptive Neural Fuzzy System Interface (ANFIS). The ANFIS does not need any prior knowledge of the system like the other NN methods. The structure is composed of 5 layers: inputs (irradiance and temperature), output and three intermediate layers which maintain the fuzzy logic system and provide the output based on the rules. Each input has three membership functions that are generated by the ANFIS method. The results show that the system is efficient to track MPP even under varying weather conditions. The method was designed with MATLAB/Simulink. The work in [40] provides a different approach using recurrent neural networks along with fuzzy logic. The structure of the networks is composed by a hidden layer, a context layer storing the results of the previous outputs of the hidden layers, the output layer (solar radiation intensity) and the input layer (voltage of PV cell and the current of PV cell at the operational point. With the solar radiation intensity and temperature, the V\({}_{MPP}\) is computed using the mathematical model. Another improvement found in this work is how a metaheuristic is used to optimize \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Algorithm** & **Features** \\ \hline GA [49; 50; 51] & Genetic Algorithms improves the results of other methods such as ANN or FPSO \\ \hline PSO [53; 54] & PSO is used to optimize Neural Network learning \\ \hline FA [56] & This algorithm is used directly to solve MPPT. It assures fast convergence with almost zero oscillations \\ \hline ABC [58; 59; 64] & In MPPT, this algorithm provides quick converge and accuracy in tracking. \\ \hline ACO [62] & ACO is used in the learning process for adjusting weights and biases or the neural networks in other to improve its results \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Metaheuristic Algorithms for MPPT. the structure and the weights/bias of the RNN.