LS-SPP: A LSTM-Based Solar Power Prediction Method from Weather Forecast Information

Solar radiation is an unlimited source of clean energy with huge exploitation potential. To effectively exploit this valuable resource, the arrival of the solar forecast has shown an improvement in incorporating renewable energy into the grid system. Having accurate solar prediction would yield useful information to ensure the power grid’s stability, gain the advantage of renewable energy, and minimize mineral resource consumption. In this paper, we introduce a novel deep learning model, namely LSTM-Based Solar Power Prediction (LS-SPP), combining long short-term memory and a recurring neural network (LSTM-RNN). The proposed model is stacked with two LSTM layers to produce a high prediction accuracy based on historical meteorological time series. Our practical experiment on real datasets shows that the LS-SSP model achieves up to 96.78% accuracy in performance, higher than the best of competitors reported about 94.19%.