# Applications of Artificial Intelligence to Photovoltaic Systems: A Review
## 3 Artificial Intelligence Applied to PV Systems
### Parameter Estimation
It operates using the 3 typical evolutionary operations (recombination, mutation and selection). The proposed variant combines CMA-ES with 2 strategies that can adjust the evolutionary directions and enrich the population diversity (Anisotropic Eigenvalue Adaptation and Local Search). The results show that the algorithm was competitive in terms of convergence efficiency and accuracy, with a mean RSME of \(9.8603\times 10^{-4}\) and Standard Deviation of \(1.6550\times 10^{-17}\) on the single-diode model and mean RSME of \(9.8402\times 10^{-4}\) and Standard Deviation of \(1.3398\times 10^{-12}\) on the double-diode model. It also had a good balance of exploration and exploitation. The simulation and experiments were implemented with MATLAB. The Whale Optimization Algorithm is a recent metaheuristic that simulates the hunting behavior of humpback whales. The basic WOA is composed of three consecutive stages: encircling prey, bubble-net attacking and searching for prey. In [116], a variant of WOA is used to estimate the parameters of a PV system. The proposed method, RLWOA, adopts a modified conversion parameter update rule and relies on the Logistic Model to balance between exploration and exploitation. This algorithm mitigates the slow convergence and ease of being trapped in local optima of the original. The results show that RLWOA performed better or at least competitively with standard WOA, other WOA variants and other metaheuristics with a mean RSME of \(9.8602\times 10^{-4}\) on single-diode. The experiments were run in MATLAB. The work in [117] presents a new optimization method called backtracking search algorithm with competitive learning (CBSA). The principle basis of BSA is composed of 4 parts: the initialization of the population, selection, genetic operators such as mutation or crossover and second selection in order to select the best candidate. The main idea of CBSA is to increase the chance of the backtracking algorithm to jump out of the local optimum by the designed competitive learning machine. Each population is divided in two subgroups, then each subgroup has three different search operations in order to update its individuals. Unlike other metaheuristics, CSBA does not need any extra control parameters. The results show the superiority of CBSA for complex optimization problems with a mean RSME of \(9.8602\times 10^{-4}\) on the single-diode model. The experimentation was performed in MATLAB. Another interesting metaheuristic is found in [118]. The author presents an advanced version of the Gray Wolf Optimizer [119] applied to parameter estimation. GWO is motivated by gray wolf behavior. Wolves are divided into four categories: Alpha wolves, which are dominant, and beta wolves, which are used to assist alpha wolves in decision making or in other activities. The order given by alpha and beta is followed by the delta wolves. Finally, the omega wolves play the role of scapegoat. The presented method is known as the Intelligent Gray Wolf Optimizer, which incorporates sinusoidal truncated functions as a bridging mechanism and opposition-based learning. The results show that the algorithm was competitive with other optimizers, with a mean error of \(4.65\times 10^{-13}\) on single-diodemono-crystalline, a mean error of 1.07 \(\times\) 10\({}^{-12}\) on double-diode mono-crystalline, a mean error of 8.50 \(\times\) 10\({}^{-12}\) on single-diode poly-crystalline and a mean error of 1.95 \(\times\) 10\({}^{-12}\) on double-diode poly-crystalline. Additionally, execution time was not compromised. A hybrid between PSO and GWO is found in [120]. The fundamental principle of this hybridization is to migrate the social thing capability of PSO with the local search ability of GWO. After performing PSO, each particle position with a certain probability is updated using the average of the three best wolves. This method reduces the drawbacks of PSO, increasing the possibility of the running of local optimums and improving the balance between exploration and exploitation.