Predict Locational Marginal Greenhouse Gas Emission Factors of Electricity with Spatial-Temporal Graph Convolutional Networks.

The electric power system is a major contributor to greenhouse gas (GHG) emissions. To reduce GHG emissions, accurate emission predictions are essential. The marginal emission factor (MEF) is a useful signal for distributed energy resource aggregators and end-use customers to mitigate GHG emissions by scheduling the flexible loads accordingly. The existing methods of locational MEF prediction often suffer from high computational burden, low prediction accuracy, and low time granularity. In this paper, we propose a hybrid machine learning framework to predict GHG emissions and locational MEF, which integrates feed-forward neural networks with spatio-temporal graph convolutional networks (STGCNs). With the power of STGCN, the proposed framework can capture the spatio-temporal pattern in power grid data. A comprehensive case study in California shows that the proposed approach outperforms the existing techniques in prediction accuracy. The proposed model provides short-term locational MEF predictions with high time granularity using only publicly available dataset.