## 5 Artificial Intelligence
### Artificial Intelligence Algorithms Integrated in PV Systems  
The photovoltaic industry is witnessing a notable advancement in the sophistication, durability, and responsiveness of its products. This is largely attributed to the integration of sophisticated systems, such as AI-based approaches and the Internet of Things (IoT). Artificial intelligence (AI) techniques have a significant impact on sustainable renewable energy, enhancing system productivity, reducing costs, and addressing complex challenges. In modern solar systems, AI algorithms are implemented to estimate daily/monthly global solar radiation from sunshine duration [58]; predict meteorological data, including solar radiance, ambient temperature, humidity, wind speed, and sunshine duration [58]; size the parameters of the PV system [59]; manage the PV system [58]; and detect broken system components [60]. Smart grid systems can handle all tasks autonomously (tracking the MPP, handling the faults, and maintaining the stability) using machine learning and deep learning approaches [61]. AI algorithms are integrated with IoT functionality, and they are part of the PV monitoring and control systems, as presented in Case 6 and Case 7. The input data for AI algorithms have different forms, such as in [27,28], based on the format of the collected data from different devices. Additionally, the inputs of AI models can be represented by images. [PERSON] [60] proposed a fault detection and classification system for PV systems based on electroluminescence images. A review of the literature reveals that there has been a growing implementation and analysis of AI-based models in the field of solar power system modelling, optimization, and sizing and the resolution of data issues [62]. In the past, the implementation of system optimization and the design of cells were based on theoretical models and empirical approaches. In the present era, engineers and researchers have devised novel procedures and contemporary solutions by leveraging the capabilities of AI to enhance the affordability, functionality, and efficacy of PV technology [63]. The literature on the implementation of AI techniques in PV systems indicates a shift in the standard approach to the future development of solar energy and PV applications, as illustrated in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **AI Model** & **Solar Energy Applications** & **Discussions and Results** \\ \hline
[64] & ANN & PV panel power prediction & The optimal configuration was identified, and the results demonstrated the efficacy of the proposed methodology for short-range power applications. \\ \hline
[65] & SVR, BPNN & PV panel energy prediction & In comparison to the traditional ANN model, the SVR and BPNN models demonstrate enhanced prediction accuracy and reduced computational complexity. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: AI methods integrated in PV systems. The efficiency of PV panels represents a crucial domain where artificial intelligence (AI) can significantly contribute to optimizing the system's operational mode. The application of AI algorithms and advanced data analytics methods has resulted in more precise measurements and evaluations of the system's parameters, which affect the efficiency of solar panels (temperature, intensity of incident light, and shading effects) [74]. In order to achieve an increase in energy production, AI algorithms are designed to have the capacity to dynamically modify the operating parameters and working conditions of solar panels by means of continuous observation and evaluation of the operating conditions in real-time. Based on real-time measurements and evaluations, the AI algorithms are capable of rapid adaptation. The optimization of operating conditions through the application of AI has resulted in enhanced overall system performance, stability, and efficiency. The generation of solar energy is contingent upon meteorological conditions [75].