一种嵌入式时空混合模型：融合多图结构与注意力驱动融合机制的单站与多站光伏功率预测

Abstract Single-site photovoltaic power prediction supports localized station management, while multi-site prediction facilitates regional grid optimization. However, most existing models focus solely on single-site prediction, with limited research addressing simultaneous multi-site forecasting. Even rarer research can both leverage information from neighboring stations for accurate single-site prediction and consider global spatiotemporal coupling for multi-site prediction. To bridge this gap, this study introduces a novel spatiotemporal hybrid model that integrates multi-graphs and attention-driven feature fusion to achieve both tasks. The model embeds Chebyshev graph convolution into bidirectional Long Short-Term Memory networks for spatiotemporal feature extraction. Using multi-graph structures, it combines multi-source heterogeneous data to extract multi-view features with enhanced granularity . Attention mechanisms dynamically refine and integrate these features, emphasizing the most critical information. Additionally, an improved dung beetle optimizer enhances hyper-parameter tuning through innovations such as chaotic initialization, search range normalization, adaptive factors, and random perturbations, ensuring robust performance. Experimental validation using datasets from eight DKASC stations in Australia demonstrated the model’s effectiveness. It consistently outperformed benchmark models in single-site prediction and achieved state-of-the-art results in multi-site forecasting, with an R2 of 0.99457 and MSE , MAE , and RMSE values of 0.01340, 0.04626, and 0.11576, respectively. These findings highlight the model’s potential for renewable energy forecasting and efficient multi-scale grid management.