Short-term Solar Power Prediction using Long Short-Term Memory in Solar Plant with Deep Learning Machine

Abstract

Solar power is a clean energy source that has developed quickly with considerable attention. Solar energy is required more accurate predictions, which could be integrated into the power grid. Therefore, this project attempts to improve short-term solar power prediction's accuracy, utilizing the long short-term memory (LSTM) in a deep learning machine. The collected data is acquired from the solar system installed in Kaohsiung city, Taiwan. The historical sequential weather parameter and the collected data from the battery module are utilized as input features for the predicting model. To acquire the optimum performance, hyperparameter optimization is employed to construct the best sequential historical data of the LSTM model. The experiment results are compared with a recurrent neural network (RNN), indicating that the LSTM could predict short-term solar power better.