# Applications of Artificial Intelligence to Photovoltaic Systems: A Review
## 3 Artificial Intelligence Applied to PV Systems
### Parameter Estimation
The results confirm the superiority of PSO-GWO compared with other competitive methods, with an RMSE of 3.06 \(\times\) 10\({}^{-3}\) and an MAE of 2.43 \(\times\) 10\({}^{-3}\) on a PV module model. In [121], a variant of the Chicken Swarm Optimization is used to solve this problem. CSO [122] is inspired by the foraging behavior and hierarchy of chicken flocks. In CSO, each chicken is considered a potential solution. The chicken flock is divided into the rooster subflock, hen subflock and chick subflock according to the fitness of each individual. Each group uses a different update mechanism to update its position. The algorithm is upgraded by using a Spiral Movement Strategy. The spiral movement allows each hen to bypass the rooster and explore a wider space instead of being limited to the search space between them. The experimental results show that the algorithm performs better in robustness and accuracy than other metaheuristics with a mean RSME of 9.8602 \(\times\) 10\({}^{-4}\) and standard deviation of 2.3517 \(\times\) 10\({}^{-12}\) on a single-diode model and a mean RSME of 9.8366 \(\times\) 10\({}^{-4}\) and standard deviation of 1.4171 \(\times\) 10\({}^{-6}\) on single-diode model.The experimentation was performed in MATLAB. Another interesting proposal is found in [123]. An Enhanced JAYA (EJAYA) is presented. The basis of the original JAYA [124] algorithm is as follows: After initializing the solutions, the algorithm identifies the best and worst solutions and modifies all the solutions based on them. All of the solutions that have better performance than the originals are kept. This process is repeated until the stop criteria are achieved. EJAYA presents three improvements: A modified evolution operator to increase the probability of approaching the victory. A simple deterministic population resizing to control the convergence rate during the search and a generalized opposition-based learning mechanism to avoid being trapped on local optima. The results indicate that the algorithm can estimate the most accurate model parameters with a mean RSME of 9.8602 \(\times\) 10\({}^{-4}\) on a single-diode model and a mean RSME of 9.8248 \(\times\) 10\({}^{-4}\) on a double-diode model. It also provided a high computational efficiency among the compared methods.The method was implemented in MATLAB. The work in [125] presents a Marine Predator Algorithm [126] applied to parameter estimation. The optimization process of MPA is divided into three main phases: The first is in high-velocity ratio or when prey is moving faster than the predator. The second is when both are moving at the same pace and the third is in a low-velocity ratio when the predator is faster than the prey. The algorithm extracted PV parameters in an accurate manner, fast speed, less time of computation and high reliability and robustness with a mean RSME of 7.73 \(\times\) 10\({}^{-4}\) on a single-diode for a France Solar cell and a mean RSME of 7.65 \(\times\) 10\({}^{-4}\) on double-diode for a France Solar cell. The examination and test occurred via MATLAB. A novel approach is found in [127] presenting a variant of P system Optimization Algorithms (POAs). POAs are helpful and reliable search techniques that abstract the structure and function of living cells. The proposed Micro-change Field Effect P System is a deeper exploration of the standard POA. The experiments showed that the method can produce solutions of high quality and has great stability with a mean RSME of 9.8606 \(\times\) 10\({}^{-4}\) on the single-diode model and a mean RSME of 9.8256 \(\times\) 10\({}^{-4}\) on the double-diode model. The method was implemented in MATLAB. In the Table 6, a summary of the reviewed models is presented.