A LSTM-EBRB-Based Ensemble Model for Photovoltaic Power Forecasting

Photovoltaic power forecasting (PPF) plays a crucial role in advancing renewable energy. With the growth grid-connected PPF systems, ensuring their safe and stable operation is becoming a key challenge. Therefore, it is imperative to accurately forecast photovoltaic power. This study presents an ensemble model based on long short-term memory (LSTM) and extended belief rule base (EBRB), so called LSTM-EBRB ensemble model, for PPF, where the LSTM and EBRB serve as base learners of the stacking ensemble framework, respectively, so that the proposed model can combine the deep learning capability of the LSTM and the fuzzy information processing capability of the EBRB to effectively capture long-term dependencies and uncertainties in PPF. To validate the effectiveness of the proposed model, experiments are conducted using the real dataset from Yumara solar system photovoltaic plant at the Desert Knowledge Australia Solar Center. The results demonstrate that the proposed model can achieves high accuracy better than six other forecasting models in the terms of three evaluation metrics: root mean square error, mean absolute error, and coefficient of determination.