## 6 Conclusions  
Optimized artificial neural network and extreme learning machine models are used to predict PV system performance under different conditions of actual dust accumulation and ambient temperature that were collected during this study. An experimental data set was collected and analyzed over almost half a year, starting on 17 March and ending on 24 September 2014. The two proposed models managed to predict the PV conversion efficiency as a function of the dust accumulation and the ambient temperature along with jumps in performance when cleaning occurs either by rainfall or manually. It was found that the optimized ELM model predicts the PV performance with a better accuracy compared to the optimized ANN model. It was found that the R\({}^{2}\) is 91.42% for the ELM model and 90.69% for the ANN model. Moreover, this work serves as an important tool for designers, researchers, operators and stakeholders because it suggests a methodology to predict reductions in PV conversion efficiency due to dust accumulation and ambient temperature along with the optimum cleaning frequency for desert and semi-arid regions in general and in Jordan and the MENA region in particular. For the period considered in this study, it was found that the average daily efficiency reductions were 0.593%/day and 0.615%/day using optimized ANN and ELM models, respectively. These are equivalent to an energy loss of 7.862 and 8.329 kWh/m\({}^{2}\), which corresponds to an energy loss due to dust accumulation of 0.789 and 0.836 US$/day, employing optimized ANN and ELM models, respectively. Additionally, this paper provides recommendations related to the optimal cleaning intervals, and it was found that the optimal cleaning interval by the optimized ELM model is 14 days, while an optimum cleaning interval of 15 days was obtained using the optimum ANN model. These recommendations take into consideration the cleaning cost and the cost loss due to dust accumulation and the electricity tariff.  
Conceptualization, W.A.-K., S.A.-D., B.H. and M.A.-A.; methodology, W.A.-K., S.A.-D.; software, S.A.-D., W.A.-K.; validation, S.A.-D., W.A.-K.; formal analysis, W.A.-K., S.A.-D.; investigation, W.A.-K., S.A.-D., B.H. and M.A.-A.; resources, B.H. and M.A.-A.; data curation, S.A.-D. and M.A.-A.; writing--original draft preparation, W.A.-K., S.A.-D. and M.A.-A.; writing--review and editing, W.A.-K., S.A.-D., B.H. and M.A.-A.; visualization, S.A.-D.; supervision, W.A.-K.; project administration, W.A.-K., S.A.-D. and B.H.  
This research received no external funding.  
This work is dedicated to the late [PERSON] who inspired us in the field of photovoltaic systems.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline \begin{tabular}{c} **Scenario** \\ **[Electricity Tariff, Cleaning Cost]** \\ \end{tabular} & **Optimal Cleaning Frequency (days)** \\ \hline \begin{tabular}{c} [0.114, 0.212] \\ \end{tabular} & 26 \\ \hline \begin{tabular}{c} [0.114, 0.952] \\ \end{tabular} & 55 \\ \hline \begin{tabular}{c} [0.114, 1.69] \\ \end{tabular} & 60 \\ \hline \begin{tabular}{c} [0.241, 0.212] \\ \end{tabular} & 18 \\ \hline \begin{tabular}{c} [0.241, 0.952] \\ \end{tabular} & 38 \\ \hline \begin{tabular}{c} [0.241, 1.69] \\ \end{tabular} & 50 \\ \hline \begin{tabular}{c} [**0.366, 0.212]** \\ \end{tabular} & **14** \\ \hline \
\begin{tabular}{c} [0.366, 0.952] \\ \end{tabular} & 31 \\ \hline
\begin{tabular}{c} [0.366, 1.69] \\ \end{tabular} & 41 \\ \hline \end{tabular}
\end{table}
Table 4: Optimal cleaning frequency of each scenario.