## 5 Artificial Intelligence
### Advantages and Disadvantages of AI for PV Systems  
The capacity of AI systems to anticipate energy production and consumption outcomes enables the implementation of optimization strategies and the attainment of enhanced efficiency. The utilization of machine learning algorithms to analyze historical data and \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **AI Model** & **Solar Energy Applications** & **Discussions and Results** \\ \hline
[66] & \begin{tabular}{c} ISCE (improved shuffled \\ complex evolution) \\ \end{tabular} & \
\begin{tabular}{c} Parameter extraction of solar \\ cell models \\ \end{tabular} &
\begin{tabular}{c} It is perceived as an accurate and effective method for \\ addressing the factor extraction challenge inherent to \\ solar cell models. \\ \end{tabular} \\ \hline
[67] & ANFIS & \
\begin{tabular}{c} Hourly worldwide, radiation \\ prediction \\ \end{tabular} &
\begin{tabular}{c} The ANFIS model demonstrates superior performance \\ in replicating the system output compared to traditional \\ arithmetic models. \\ \end{tabular} \\ \hline
[68] & ML, SVR & \
\begin{tabular}{c} Global solar radiation \\ prediction \\ \end{tabular} &
\begin{tabular}{c} The experimental results demonstrate a satisfactory \\ estimation for Egypt and provide evidence of the \\ effectiveness of support vector regression (SVR) models. \\ \end{tabular} \\ \hline
[69] & GA & Forecasting of solar power & It was observed that there were significant fluctuations \\ in & & in the results obtained in terms of authentic power \\ output, with considerable variation occurring within a \\ \hline
[70] & GA-BPNN & \
\begin{tabular}{c} Prediction of mean \\ temperature \\ \end{tabular} &
\begin{tabular}{c} The most optimal outcomes were observed when BPNN \\ and GA-BPNN were integrated. This approach may \\ facilitate the development of expedient and pragmatic \\ models for forecasting outdoor heat environments. \\ \end{tabular} \\ \hline
[71] & GA & \
\begin{tabular}{c} Maximum power point \\ tracking (MPPT) solar \\ tracking \\ \end{tabular} &
\begin{tabular}{c} The device is employed for the purposes of tracking and \\ control. A microcontroller was designed to facilitate the \\ implementation of the GA tracking technique. It was \\ observed that the system’s implementation \\ was effective. \\ \end{tabular} \\ \hline
[72] & BPNN & \
\begin{tabular}{c} Diffuse solar radiation \\ prediction \\ \end{tabular} &
\begin{tabular}{c} The BPNN technique employs a set of characteristics \\ that facilitate the generation of precise evaluation \\ indices. These include data pertaining to the month of \\ the year, mean temperature, daily duration, wind speed, \\ humidity (relative), rainfall, and daily worldwide. \\ \end{tabular} \\ \hline
[73] & ANN and TRNSYS & \
\begin{tabular}{c} Prediction of performance of \\ industrial control systems \\ (ICS) \\ \end{tabular} &
\begin{tabular}{c} It has the potential to supplant the necessity for such \\ tests entirely. The system allows for the simulation of \\ individual components and the generation of reports \\ based on the configured parameters. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._weather patterns facilitates the forecasting of energy output and the subsequent adjustment of system settings in accordance with this analysis. By monitoring and reacting to changes in energy demand in real-time, AI-controlled PV systems ensure the efficient and cost-effective utilization of energy [76]. While AI can be beneficial for PV systems, it is important to consider the potential drawbacks, as outlined in Table 4. For example, generation discontinuity is caused by seasonal fluctuations. There are challenges and open issues pertaining to the application of artificial intelligence (AI) approaches in renewable energy systems, including photovoltaics [77].