This paper presents a novel graph-based deep learning framework for accurate and efficient voltage state estimation across diverse power distribution networks. The proposed model integrates topology-aware encoding, temporal memory propagation, and uncertainty-aware decoding within a federated learning architecture, enabling scalable deployment under partial observability and communication constraints. Experiments on three benchmark datasets, IEEE 33-Bus, IEEE 123-Bus, and European LV, demonstrate that the proposed method consistently outperforms state-of-the-art baselines. Specifically, it achieves the lowest average RMSE of 0.25, 0.31, and 0.29 across the three systems, representing up to a 32.4% reduction compared to standard GNNs. MAE is similarly improved to 0.18, 0.22, and 0.21, while MAPE is reduced to 1.5%, 2.0%, and 1.7%. Beyond accuracy, the model demonstrates superior efficiency, requiring only 87M FLOPs and 448MB of peak VRAM, and converging 30% faster than comparable baselines. Ablation studies confirm the essential role of each architectural component, and uncertainty visualization validates the model’s calibrated confidence across time and topology. The proposed approach balances predictive performance, computational tractability, and robustness, providing a promising solution for next-generation intelligent grid state monitoring under real-world constraints such as sensor sparsity, data heterogeneity, and edge deployment limitations.
Zhang et al. (Wed,) studied this question.