Design of Photovoltaic Power Generation Prediction Model with Recurrent Neural Network

Hanho Kim, Haesung Tak, Hwan-gue Cho

http://doi.org/10.5626/JOK.2019.46.6.506

The Smart Grid predicts the power generation amount of renewable energy and enables efficient power generation and consumption. Existing PV power generation prediction studies have rarely applied and compared recurrent neural network techniques that are superior to time series. Furthermore, in the reported studies, there is no consideration of the length of past data used for learning, leading to lowered prediction performance of the model. In this study, we used the embedded variable selection techniques to find the factors influencing PV power generation. Subsequently, experiments were carried out to insert various past data length into the recurrent neural networks (RNN, LSTM, GRU). We found the optimal prediction factors and designed a prediction model based on the outcomes of the experiments. The designed PV power generation prediction model shows better prediction performance compared to other factor settings. In addition, better performance based on the prediction rate is confirmed in the present study as compared with the existing researches.