Enhanced PV power prediction using LSTM-integrated soft actor–critic model based on long short-term memory

Abstract

Accurate PV power prediction is crucial in efficiently operating intelligent power grid systems. Data-driven approaches have shown high performance in predictive tasks. Deep reinforcement learning (DRL) merges deep learning with reinforcement learning and has been widely studied for optimization challenges in various fields. However, limited research has focused on applying DRL to ultra-short-term PV power prediction. Hence, a soft actor–critic (SAC) model using long short-term memory (LSTM) is proposed for predicting PV power. To accomplish this, first, the PV power problem is modeled as a Markov decision process with historical weather data and PV power data as state inputs. Then, LSTM is integrated into the critic network of SAC to enhance its memory capability, thus improving prediction accuracy. Ultimately, the agent engages with the environment to address the optimization problem. Experimental results indicate that the proposed model attains greater prediction accuracy. This study explores the potential of DRL for PV power prediction, and the proposed method can be extended to other prediction fields, including grid prediction and wind power prediction.