Graph Convolution Network for Carbon Emission Factor Forecast of Power Grid

Global climate change is intensifying, and more measures to reduce carbon emissions should be taken. A method based on graph neural network can predict the carbon emission factors of power grid and that helps the users side to take more active measures to reduce the carbon emission behavior caused by electricity consumption. By learning the data of power grid loads and carbon emission factors through the graph neural network, which quickly predicts the carbon emission factors of nodes of the power grid, and provides an auxiliary basis for power grid dispatching. Finally, using a standard IEEE 39-bus system, the validity of the prediction model is verified by comparing the prediction data with the data obtained from traditional power flow calculations.