# A Predictive Reliability Model to Assess the Performance of Photovoltaic Systems
## 4 Conclusions  
In conclusion, this work proposed a method to predict some statistics to assess the quality of the PV systems. To achieve this goal, data collected from two different geographical regions were used to simulate the PV system work. The simulation results showed that the higher the temperature, the lower the PV system efficiency. This is valid for the change of temperature along the year and among different locations.  
From the collected inputs, obtained device factors and system responses, the most comprehensive variables were selected to undergo a Taguchi OA experiment to obtain the dependency of the system responses on the inputs and device factors. This algorithm obtained the level of significance of each of the inputs and devised factors on the system responses. In turn, it helped to choose the best system response that could be used for reliability analysis. The Taguchi OA showed that the PV system efficiency and the produced energy are highly dependent on the ambient temperature and the global effective radiation, respectively. It also revealed that the performance ratio of the PV system varies with many inputs and device parameters, which makes it a reliability analysis candidate.  
The reliability analysis applied to the performance ratio of the PV system showed that the field orientation (30\({}^{\circ}\), 20\({}^{\circ}\)) leads to the longest lifetime of the PV system in the near Cairo region, while the field orientation (30\({}^{\circ}\), 30\({}^{\circ}\)) leads to the longest lifetime of the PV system in the near Riyadh region.  
Conceptualization, N.S. and A.E.B.; methodology, N.S.; software, A.E.B.; validation, A.E.B.; formal analysis, N.S.; investigation, N.S.; resources, A.E.B.; writing--original draft preparation, N.S.; writing--review and editing, N.S.; visualization, N.S.; supervision, N.S.; project administration, N.S.; funding acquisition, N.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Deanship of Scientific Research, King Faisal University, United Arab Emirates (grant number NA000138).  
Not applicable.  
The reported data-supporting results can be found at this link. The input data presented in this link are read by the software PVsyst from the databases. However, we cannot access the database itself. All simulation data and the results can be found here. [[https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) PGB4 dzEkn7 IVHH7 xW2 pndeWybn3 kYGVc]([https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) PGB4 dzEkn7 IVHH7 xW2 pndeWybn3 kYGVc) (accessed on 8 February 2022).  
The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through project number NA000138.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Statistic** & **30\({}^{\circ}\) Tilt, 0\({}^{\circ}\) Azimuth** & **30\({}^{\circ}\) Tilt, 20\({}^{\circ}\) Azimuth** & **30\({}^{\circ}\) Tilt, 30\({}^{\circ}\) Azimuth** \\ \hline \(R\) (\(t=100\)) & 0.865548 & 0.865905 & 0.884444 \\ \hline \(Mean\_Life\) & 685.63 months & 687.59 months & 688.36 months \\ \hline \(Failure\_Rate\) & 0.001459/months & 0.001454/months & 0.001228/months \\ \hline \(Reliable\_Life:\) (\(t|R=\) 0.9 ) & 73.238 months & 73.455 months & 85.801 months \\ \hline \(Reliable\_Life:\) (\(t|R=\) 0.5) & 476.244 months & 477.605 months & 564.470 months \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results obtained by the reliability analysis for the near Riyadh region.