| Title |
The Prediction of a Floating Photovoltaic Generation Utilizing RNN |
| Authors |
권오극(Ogeuk Kwon) ; 최수현(Soohyun Choi) ; 조현식(Hyunsik Jo) ; 차한주(Hanju Cha) |
| DOI |
https://doi.org/10.5370/KIEE.2022.71.8.1126 |
| Keywords |
Recurrent Neural Network; Power Generation Prediction; Floating Photovoltaic; LSTM; AI |
| Abstract |
Renewable energy has advantages in environmental aspects such as greenhouse gas reduction and fine dust reduction, but the stability of the power system is lowered due to the variability & uncertainty of renewable energy sources. Therefore, it is important to predict the amount of generation of renewable energy, which can contribute to system stabilization. In order to predict the power generation of floating photovoltaic(FPV), the generation amount of 500㎾ FPV and meteorological data were used to predict the power generation through the Recurrent Neural Network(RNN). To perform appropriate prediction, identifying the correlation between variables, removing multicollinearity, handling missing values properly are performed and the amount of power generation is predicted through appropriate RNN. In addition, it is analyzed how the influence of wind affects the amount of power generation of FPV |