# 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
The results show an improvement over other competitive methods. Another important hybrid is the method in [68], which combines ANN with Support Vector Machines (SVM) [69]. The Course Gaussian Support Vector Machine (CGSVM) is used to improve the dataset before sending the dataset to the neural network. The CGSVM is a type of nonlinear SVM and is usually used on optimization tasks. The NN was composed of 2 inputs (temperature and irradiance), 1 output (PV current) and a single hidden layer with 13 neurons. The results are slightly worse on power than the ANFIS, but the required time was significantly less than the ANFIS. The work presented in [41] shows a new way of optimizing the ANN for MPPT. The ANN is composed of a 2-3-3-1 structure in order to make real-time applications and to avoid memorization events. The ANN takes input as irradiance and temperature and gives output as maximum voltage and is optimized with a metaheuristic called FPSOGSA. The method compares different activation functions in order to maximize performance. The results are compared with P&O and traditional NN; it is found that the method provides more stability and efficiency. A similar approach is presented in [70]. A PSO algorithm is used to find the best topology, find the best 20 hidden nodes and to optimize the initial weights of the neural network. Two inputs are used (G--level of irradiance and T--temperature), and a single output (predicting power of PV array at MPP). The model proved to be more effective under various weather conditions than other ANN or FLC techniques. The work in [61] attempts to optimize an ANN using ACO. Using this algorithm for ANN training results in quicker training. Tangent sigmoid is chosen as the activation function. Six different topologies were evaluated in order to find the best structure, the best being a single hidden layer with 20 neurons. Two inputs are transmitted to the ANN, PV array voltage and current. The output is set as the duty cycle (d). The model tracks MPP efficiently even under irradiation changes. The approach found in [71] uses the concept of Deep Reinforcement Learning (DRL), which tries to implement Reinforcement Learning (RL) through NN. The advantage of RL for MPPT is that RL techniques are model-free, they do not require knowing the behavior of the PV source or predefining its dynamics. A continuous state space is defined, corresponding to the current (I) values. The action space is also continuous, so it contains all the actions that can be applied to generate a change in the system. Finally, the reward function is computed directly proportional to the power, and no prior knowledge about the system is needed to define it. The system uses four networks, one for computing the policy, one for the critic and two called targets that are used to stabilize the learning procedure. The model can learn highly efficient policies from scratch, and the results show higher performance than other models. With the aim of improving ANFIS, a new hybrid was proposed in [42]. The ANFIS is trained using the BAT algorithm [72]. The use of metaheuristic improves the training of metaheuristic compared with back-propagation; the BAT algorithm provides better convergence, simplicity and faster tracking speed than other techniques. The results show an improvement over standard ANFIS or ANFIS optimized with other metaheuristics such as PSO. Similar work is found in [73], where an ANFIS-CPHO is presented. The Crowded Plant Height Optimization [74] is in charge of training the ANFIS. The results are compared with standard ANFIS and show an increase in the speed and efficiency of the tracker. A summary of the analyzed models in this section can be found in Table 4.