Dynamic Graph Attention Meets Multi-Scale Temporal Memory: A Hybrid Framework for Photovoltaic Power Forecasting Under High Renewable Penetration

In the context of the accelerated global energy transition, power fluctuations caused by the integration of a high share of renewable energy have emerged as a critical challenge to the security of power systems. The goal of this research is to improve the accuracy and reliability of short-term photovoltaic (PV) power forecasting by effectively modeling the spatiotemporal coupling characteristics. To achieve this, we propose a hybrid forecasting framework—GLSTM—combining graph attention (GAT) and long short-term memory (LSTM) networks. The model utilizes a dynamic adjacency matrix to capture spatial correlations, along with multi-scale dilated convolution to model temporal dependencies, and optimizes spatiotemporal feature interactions through a gated fusion unit. Experimental results demonstrate that GLSTM achieves RMSE values of 2.3%, 3.5%, and 3.9% for short-term (1 h), medium-term (6 h), and long-term (24 h) forecasting, respectively, and mean absolute error (MAE) values of 3.8%, 6.2%, and 7.0%, outperforming baseline models such as LSTM, ST-GCN, and Transformer by reducing errors by 10–25%. Ablation experiments validate the effectiveness of the dynamic adjacency matrix and the spatiotemporal fusion mechanism, with a 19% reduction in 1 h forecasting error. Robustness tests show that the model remains stable under extreme weather conditions (RMSE 7.5%) and data noise (RMSE 8.2%). Explainability analysis reveals the differentiated contributions of spatiotemporal features. The proposed model offers an efficient solution for high-accuracy renewable energy forecasting, demonstrating its potential to address key challenges in renewable energy integration.