| CPC G06Q 50/06 (2013.01) [G06N 3/08 (2013.01); G06N 20/00 (2019.01)] | 20 Claims |
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1. A method of using machine-learning for calculating consumption-based carbon emissions in multiple interconnected power grids, the method comprising:
receiving, 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;
receiving, from a second local grid:
at least a second power output quantity; and
a carbon intensity of the second local grid;
training, 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;
generating, 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
displaying, 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.
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