Abstract
The main contents of this paper are to verify the environmental factors affecting the power generation of floating photovoltaic systems and to present the power generation prediction model considering environmental factors by using regression analysis and neural networks studied during the last decade. This study focused on a comparative analysis of which model is best suited for the power generation prediction of the floating photovoltaic (PV) system. To compare the power generation characteristics of a floating and a land-based PV system, two identical 2.5 kW PV systems were installed—one on the water surface in the Boryeong Dam, Korea, and the other nearby on dry land—and their performances were compared. The solar irradiance of the floating PV system was 1.1% lower than that of the 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. Using the correlation analysis of data mining techniques, environmental factors affecting the efficiency of the floating PV system were investigated. The correlation coefficient between the module temperature and water temperature was r = 0.6317 which proves that the high efficiency and low module temperature characteristics of the floating PV system, when compared with that of the land-based PV, are due to the water evaporation effect. Considering environmental factors, power-generation prediction models based on regression analysis and neural networks are presented, and their accuracies are compared. This comparison confirms that the accuracy of the power generation prediction model using neural networks was approximately 2.59% 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.
Highlights
The renewable energy market has been growing rapidly, owing to fossil fuel reduction and environmental problems [1,2]
This comparison confirms that the accuracy of the power generation prediction model using neural networks was approximately 2.59% 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
Summary
The renewable energy market has been growing rapidly, owing to fossil fuel reduction and environmental problems [1,2]. The solar power market for large-scale PV power-generation systems with capacities in the range of MW or higher is expanding rapidly. As of 2020, the representative floating PV systems operating in Korea are a 2 MW system on the Boryeong Dam reservoir, a 3 MW system on the Chungju Dam reservoir, and an 18.7 MW system on the lagoon at the Gunsan Industrial Complex. 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. 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]. Neural network analysis is being widely used for estimating the generation power because of its more accurate prediction when compared
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