## 1 Introduction
From Equation (1), it can be expected that the amount of power generation decreases by 0.45% as the module temperature increases by 1 \({}^{\circ}\)C from 25 \({}^{\circ}\)C.  
Published research has concluded that the power generation of floating PV systems is 10 to 15% greater than that of the land-based PV systems because of the cooling effect [12,13]. In one study, a PV module cooling system with intermittent water spray to module was installed to get the above mentioned result, and in another, the solar-cell module temperature was presumed to be reduced by the evaporation of water. This paper investigates the PV systems without any mechanical cooling system and with the same sizes and nearby locations, which is rarely reviewed in the other papers. In addition, this paper proposes how to calculate the generated power by a machine learning algorithm, which is not done much in floating PV systems. The various factors used for comparing the land and floating PV systems were different; for example, the modules, inverters, years of installation, and power-generation capacities were different. Therefore, there was a limit to the precise comparison of the power-generation characteristics of the two types of PV systems. To develop a large-scale floating PV power plant, the economic feasibility must be secured, and the most important factor in analyzing the economic feasibility is the power-generation prediction. The land-based PV system output forecasting problem has been thoroughly studied during the last decade using machine or neural network-based learning. However, the installation environmental of floating PV systems is different from that of land-based PV systems. Therefore, this paper focuses on selecting the most suitable power generation prediction model, considering the power generation characteristics of floating PV system. Two identical 2.5 kW PV systems were installed on the water surface of the Boryoung Dam reservoir, Korea, and on the nearby land, respectively, to ensure precise comparison [14]. Based on the operation results over six months, the following facts were confirmed: The solar irradiance of the floating PV was 1.1% less than that of land-based PV. Nevertheless, the floating PV module temperature was 4.9% lower than that of the land-based PV, generating approximately 3% more power. The correlation coefficient between the module temperature and water temperature was \(r=0.6317\), which proved that the high efficiency and low module temperature characteristics of  
Figure 2: Three MW floating PV systems on the reservoir of Chungju Dam, Korea (source: K-water). the floating PV, when compared to that of the land-based PV, were due to the water evaporation effect. Through regression analysis, the power-generation prediction model of a floating PV system considering environmental factors was presented. Recently, neural network analysis is being widely used for estimating the generation of power because of its more accurate prediction when compared to regression analysis [15]. Therefore, a prediction model using neural networks, represented by a nonlinear algorithm, was developed in this study. A hyperbolic tangent was used as the activation function, and prediction models were compared using one to twenty nodes to choose the best model. In this study, prediction models for floating PV power generation, considering environmental factors, are presented using regression analysis and neural networks. Upon comparing the accuracy, it was confirmed that the prediction model using neural networks was more accurate than the one using regression analysis. Therefore, this study presents more logical and accurate approaches to power generation prediction and economic feasibility analysis, which are essential for the development of large-scale floating PV power plants.