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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 87 M FLOPs and 448 MB 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\u2019s 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.<\/jats:p>","DOI":"10.1142\/s0218001425500430","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T05:57:52Z","timestamp":1765346272000},"source":"Crossref","is-referenced-by-count":0,"title":["A Graph Neural Network-Based State Perception Model for Digital Twin Power Grids"],"prefix":"10.1142","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0386-2457","authenticated-orcid":false,"given":"Chunmei","family":"Zhang","sequence":"first","affiliation":[{"name":"Zhongshan Power Supply Bureau Guangdong Power Grid Co., Ltd. Zhongshan, Guangdong 528400, P. R. 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