Photovoltaic power prediction based on reinforcement learning and hybrid deep learning models

The prediction of photovoltaic power is of great significance for the safe operation of photovoltaic grid-connected systems. This paper proposes a photovoltaic power prediction method based on reinforcement learning and hybrid deep learning models. Firstly, we apply reinforcement learning algorithms to photovoltaic power prediction, continuously optimizing the model through the interaction between the agent and the environment to improve the accuracy and stability of power prediction. Secondly, we combine deep learning models, extracting spatiotemporal features through LSTM and GRU networks to effectively model photovoltaic generation data. Finally, we combine reinforcement learning and deep learning models to construct an effective photovoltaic power prediction framework. Experimental results show that this model has significant advantages in accuracy and generalization of power prediction, providing strong support for the operation and management of photovoltaic power generation systems. This research is of great significance for promoting the intelligent development of the photovoltaic power generation industry.