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Arxiv
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Heterogeneous Graph Neural Networks for Short-term State Forecasting in Power Systems across Domains and Time Scales: A Hydroelectric Power Plant Case Study
- Accurate short-term state forecasting is crucial for efficient and stable operation of modern power systems impacted by renewable energy sources.
- Graph Neural Networks (GNNs) are effective for system state forecasting by leveraging sensor network structures.
- Heterogeneous Graph Attention Networks are proposed to model both homogeneous and heterogeneous sensor data relationships in multi-domain power systems.
- Experimental results show that the proposed approach outperforms conventional methods by 35.5% in power system state forecasting accuracy.
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