JamesTwallin/GridSeis
Predicting Carbon Intensity from Grid Frequency Data Using FFT Analysis and Gradient Boosted Regression
This project helps energy analysts and smart device developers estimate how carbon-intensive the electricity grid is, solely by looking at grid frequency data. It takes in raw, 1-second electrical grid frequency data and outputs a predicted carbon intensity value for that time period. This is useful for anyone needing to understand the real-time environmental impact of electricity without direct access to detailed generation mix information.
Use this if you need to estimate carbon intensity in real-time or in regions with limited data access, enabling applications like smart devices that optimize electricity usage during cleaner power periods.
Not ideal if you require precise, verified carbon intensity data from official sources, as this provides an estimation based on an indirect signal.
Stars
29
Forks
1
Language
Python
License
MIT
Category
Last pushed
Dec 10, 2025
Commits (30d)
0
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