## 5 Conclusions  
In this study, the power-generation prediction model for a floating PV system using neural networks was compared and analyzed with one based on regression analysis. The summary of the characteristics of the floating solar power generation model verified in this study is as follows:  
* The existing knowledge that the cooling effect and efficiency of the floating PV system increased with the evaporation of water from the surface was supplemented by more objective and concrete analyses. Based on the operation of two 2.5 kW land-based and floating PV systems installed near and on the water surface of the Boryeong Dam, Korea, respectively, the results were analyzed comparatively by applying a statistical probability method. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**MAPE**} & \multirow{2}{*}{
\begin{tabular}{c} **Linear** \\ **Regression** \\ \end{tabular} } & \multicolumn{3}{c}{**Number of Hidden Nodes of Neural Network**} \\ \cline{3-6}  & & **1** & **5** & **10** & **20** \\ \hline
**Training** & 9.61\% & 10.25\% & 8.78\% & 6.98\% & 7.54\% \\ \hline
**Test** & 11.35\% & 12.17\% & 10.36\% & 8.8\% & 11.35\% \\ \hline \end{tabular}
\end{table} 
Table 6: MAPE of training and test dataset, obtained using algorithms. Figure 25: Neural network analysis results (test data). * During the period of analysis, the amount of solar irradiation for the land-based PV system was approximately 1.1% higher than that of the floating PV system; however, the average module temperature of the floating PV system was 4.9% lower than that of the land-based PV system. * To analyze the reason behind the module temperature of the floating PV system being lower than that of the land-based PV system, the correlation between the module temperature difference and the air-water temperature, as well as the module temperature and wind speed, were analyzed. From the results, it was shown that there was a positive correlation between the module temperature difference and the land and water temperature difference, with a correlation coefficient of 0.6451. On the other hand, there was no correlation between the module temperature difference and the wind speed, with a correlation coefficient of 0.0032. Based on the operational data, the regression analysis method suggested a characteristic function that could predict the power generation, even considering solar radiation and module temperature. * This study proved that the efficiency of a floating PV system was higher than that of a land-based PV system, owing to the cooling effect of water, although there are some differences, according to the environmental factors of the area. Using the water temperature characteristics, it was possible to predict the amount of electricity generated more accurately. * In addition, the accuracy of the power generation prediction model using neural networks was approximately 2.91% higher than that of the regression analysis method. As a result of adjusting the hidden nodes in the neural network algorithm, it was confirmed that a neural network algorithm with ten hidden nodes was most suitable for calculating the amount of power generation
* This paper is the result of utilizing 2.5 kW research equipment installed in the dams and nearby areas of South Korea. Therefore, as a result of this study, it is possible to more accurately predict the power generation of the floating PV system in an environment similar to these study conditions, such as installation locations, system scales and configurations, etc. Conceptualization, H.S.J. ([PERSON]); methodology, H.S.J. ([PERSON]) and H.H.L. ; writing-original draft preparation, H.S.J. ([PERSON]); writing-review and editing, J.C.; software, H.H.L. and H.S.J. ([PERSON]); supervision, J.C.; funding acquisition, H.S.J. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No.