Methods and systems for machine-learning for prediction of grid carbon emissions

A system for using machine-learning for calculating consumption-based carbon emissions in multiple interconnected power grids, the system comprising a computing device configured to:

• receive, from a first local grid monitoring device, a plurality of first power output quantities, wherein each first power output quantity of the plurality of first power output quantities represents power output generated in a first local grid;
• receive, from a second local grid:
• at least a second power output quantity;
• and
• a carbon intensity of the second local grid;
• train, by a computing device, an emission estimation machine-learning process using training data entries, wherein the training data entries comprise correlations between past power output quantities and reported carbon emission data;
• generate, using the emission estimation machine-learning process, a first plurality of projected carbon emission rates, wherein each of the first plurality of projected carbon emission rates is generated as a function of the first plurality of power output quantities and the at least a second power output quantity;
• and
• display, on a user interface, a consumption-based carbon emissions calculation describing carbon emissions consumed per megawatt-hours (MWh) produced by the first grid and the second grid based on solving a linear system of equations.