Kubernetes Carbon Intensity Exporter

Display at-a-glance data of carbon intensity or Octopus Agile prices on a Pimoroni Blinkt! display or a Pimoroni Inky pHAT display.

A simple Rust library to retrieve data from https://api.carbonintensity.org.uk/

R wrapper for National Grid Carbon Intensity API

Unofficial Home Assistant integration based on data supplied by carbonintensity.org.uk

Unofficial open source implementation of the Carbon Intensity Indicator (CII) by the International Maritime Organization (IMO), as outlined in Resolution MEPC.354(78)

Unofficial open source python implementation of the Carbon Intensity Indicator (CII) by the International Maritime Organization (IMO), as outlined in Resolution MEPC.354(78)

Web App for the Japan Grid Carbon API

Learn to optimize machine learning tasks for environmental sustainability. Discover how to use real-time electricity data and low-carbon energy sources for model training and inference, reducing the carbon footprint of your cloud operations.

This tool provides a real-time and historical perspective on the carbon intensity of electricity consumed within Great Britain, offering academic and industry insights.

Single-node dispatch and investment model for the power sector

Carbon Intensity Simple Client Library

Python library for Green Computing - uses carbon intensity APIs to make code execution low-carbon

.NET UK Carbon Intensity API. Data provided by the National Grid

R package providing scripts and functions to download and process UK and NZ electricity generation data

Get the carbon intensity of your electricity grid into homebridge

Diving into the world of Tracking CO2 Emissions from our software or code. Code Carbon is a lightweight open-source Python Library that lets you track the Co2 emissions produced by running code.

Gathering and analyzing electricity generation data from the ENTSO-E portal

web extension for chrome/firefox to monitor how much co2 was generated during your navigation

A real-time Streamlit dashboard that analyzes and forecasts carbon intensity in the UK using machine learning models like Random Forest and SGD. Includes dynamic anomaly detection, environmental impact scoring, and actionable green recommendations to promote sustainable energy practices.