## 6 Cybersecurity  
The expansion of photovoltaic systems within the energy sector is driven by three key factors: technological advancements, cost-effectiveness, and environmental concerns. Two-way communication technology and computational intelligence are employed by smart PV systems to facilitate the integration of the entire energy system, encompassing the full range of generation and consumption endpoints. Despite the multitude of advantages, this approach renders photovoltaic systems susceptible to security threats, thereby pro \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **AI Model** & **Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{[64]} & \multirow{3}{*}{ANN} & The model is advantageous due to its simple architecture and these algorithms play an important role for remote management of PV systems. & The AI model requires long training time, which is the limiting factor. \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \multirow{3}{*}{[65]} & \multirow{3}{*}{SVR, BPNN} & Accurate forecasting with less computational modeling comparing with other models. & Average forecasting errors. \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \multirow{3}{*}{[66]} & \multirow{3}{*}{ISCE} & It gave a sound solution to the parametric extraction issue and the ISCE algorithm obtained the best results for parameters extraction from PV cells and panels. & N/A \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \multirow{3}{*}{[67]} & \multirow{3}{*}{ANFIS} & It gives accurate and feasible predictions. & The algorithm’s performance is dependent in the quality of input data. \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \multirow{3}{*}{[68]} & \multirow{3}{*}{ML, SVR} & Good estimation and accuracy. & Errors in the training and testing datasets. \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \multirow{3}{*}{[69]} & \multirow{3}{*}{GA} & Ability to source the spot standards for power prediction. & Complexity of the system and computational resources required for the algorithm to provide accurate forecasting. \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \multirow{3}{*}{[70]} & \multirow{3}{*}{GA-BPNN} & This approach can lead to the prediction of outdoor mean temperature using reduced computational resources. & Preparation of datasets used for training and testing could be difficult. \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \multirow{3}{*}{[71]} & \multirow{3}{*}{GA} & The primary advantage is that a non-conventional approach was successfully simulated to provide solar tracking under the same atmospheric changes. & The extraction of the maximum power of the PV panel when the PV panels are partly shaded. \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \multirow{3}{*}{[72]} & \multirow{3}{*}{BPNN} & The algorithm has accuracy and it is cost-effective. & The estimation of error in the input variables. \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \end{tabular}
\end{table} 
Table 4: AI methods—advantages and disadvantages. viding hackers with novel avenues for exploitation of vulnerabilities. The expansion and complexity of photovoltaic systems may render them susceptible to cyber threats, which could potentially disrupt operations and impact energy infrastructure.