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Graph-temporal convolutional network for steam heating network simulation considering dynamic characteristics
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DOI：10.1016/j.energy.2025.137567.png)
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Abstract
En 中文
• Proposing AI-based approaches for steam system modeling from a graph perspective. • Physics-based imputation improves missing data with explainable analysis. • Fluid mechanics-guided edge weights enhance GNN performance interpretably.
Keywords:
AI-based modeling
steam system
graph neural networks
physics-informed imputation
fluid mechanics-guided weights