Predicting Real-Time Carbon Emissions for Power Grids Using Graph Convolutional Networks

Accurate prediction of carbon emissions is crucial for both providing effective carbon reduction guidance to the power grid sector and driving society-wide carbon emission reduction. Existing methods based on power flow calculation theory heavily rely on real-time grid parameters and cannot accurately predict grid carbon emissions. To address the challenge, this paper proposes a novel spatiotemporal prediction model by integrating graph convolutional networks (GCNs) and temporal convolutional networks (TCNs). The GCN layer enhances node representation by aggregating neighborhood information, while the TCN layer captures long-term temporal dependencies through expanded causal convolutions. Experiments conducted on the IEEE 14-bus system demonstrate that the model achieves high accuracy and real-time processing capabilities, confirming its robustness and practical value for dynamic carbon emission predictions.