## 5 Artificial Intelligence  
Artificial intelligence (AI) algorithms are known for their ability to simulate human intelligence and problem-solving capabilities. The AI models learn from examples, are able to work with incomplete data, and, once trained, can perform prediction at high speeds. The machine learning algorithms are classified into three categories [52]: supervised learning, unsupervised learning, and reinforcement learning. The neural network (NN) learning algorithms are part of the deep learning category, and they were the most used learning algorithms in several literary works [53]. Their working mode is as follows: nodes in one layer are connected to nodes in the next layer. Neural networks have three types of layers: input layers, hidden layers, and output layers, as presented in Figure 8 [54]. The number of hidden layers may vary.  
The next rule should be respected to ensure a neural network is properly trained and tested: the number of outputs of a layer should be equal with the number of inputs of the next layer. An activation function could be implemented as in [55] where was used ReLU activation function to easily train the model and obtained the desired performances and results.  
The following are some AI techniques which can be used in the photovoltaics technology:  
* Artificial Neural Network (ANN)
* Support vector-machine (SVM)/Support vector-regression (SVR)  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **MPPT Method** & **Description** \\ \hline
[47] & Perturb and Observe (P\&O) & The proposed algorithm is based on the utilization of voltage sensors. The results of the simulation and experimental studies demonstrated that the proposed method was effective in enhancing the dynamic performance of the PV system. \\ \hline
[48] & Fuzzy particle swarm optimization (FPSO) & A hybrid FPSO was designed with the objective of achieving frequency stabilization under a range of loading conditions. It was determined that the frequency stability was enhanced when the fuzzy logic controller (FLC) method was employed in conjunction with the hybrid FPSO. \\ \hline
[49] & Improved Perturb and Observe (IP\&O) & An enhanced P\&O MPPT algorithm was modelled using MATLAB/SIM-ULINK software. The efficiency obtained in the tests was 99.7\%. \\ \hline
[50] & Cuckoo Search (CS) & The research compared the particle swarm optimization (PSO), incremental conductance (INC), and CS methods. Under shading conditions, CS has better results than PSO. \\ \hline
[51] & Constant voltage (CV) & The algorithm designed automatically modifies the reference voltage, taking into consideration the changing meteorological conditions. \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 8: Layers of a neuronal network.  
* Generic Algorithm (GA)
* Bootstrap Aggregated Neural Networks (BANN)
* Back-propagation neural network (BPNN)
* Adaptive Neuro-Fuzzy Inference System (ANFIS)
* Naive Bayes (NB)
* Historical Similar Mining (HISIMI)
* Transient System Simulation Tool (TRNSYS)  
The advent of intelligent and smart PV systems, especially those based on AI, has led to a growing popularity of PV systems for energy production and consumption. This is due to their ability to handle new opportunities and issues in photovoltaics, such as power management [56]. Various studies have been published recently that highlight the importance of AI techniques in this field. These include prediction of solar radiation, prediction of energy production, solar energy modelling, sizing of photovoltaic systems, and electrical load prediction [57].