Values are the average, max, and min Carbon Intensity in gCO2/kWh for each day
National Grid's Carbon Intensity API provides an indicative trend of regional carbon intensity of the electricity system in Great Britain (GB) 96+ hours ahead of real-time. It provides programmatic and timely access to both forecast and estimated carbon intensity data.
The Carbon Intensity forecast includes CO2 emissions related to electricity generation only. The includes emissions from all large metered power stations, interconnector imports, transmission and distribution losses, and accounts for national electricity demand, embedded wind and solar generation.
The goal of this API service is to allow developers to produce applications that will enable consumers and/or smart devices to optimise their behaviour to minimise CO2 emissions.
The carbon intensity of electricity is a measure of how much CO2 emissions are produced per kilowatt hour of electricity consumed.
The 'actual' value (grey line) is the estimated carbon intensity from metered generation. The 'forecast' value (blue line) is our forecast. The carbon intensity of electricity is sensitive to small changes in carbon-intensive generation. Carbon intensity varies by hour, day, and season due to changes in electricity demand, low carbon generation (wind, solar, hydro, nuclear, biomass) and conventional generation.
Carbon Intensity Forecast (-24hrs to +48hrs)
Select a start date and end date in the boxes below in the format DD/MM/YYYY. Data is retrieved in UTC time and is returned as a CSV file. Only 31 days of data can be downloaded at a time. Data cannot be downloaded between years. Data is only available after 26/09/2017.Start: End:
Current GB Generation Mix
WWF have implemented the API into a re-usable widget that can help people plan their energy use, switching devices on when energy is green and off when it’s not.
National Grid forecasts the carbon intensity and generation mix of electricity consumed across 14 geographical regions in Great Britain. The spatial and temporal characteristics of carbon intensity can be observed in the map below.
The boundaries are defined according to Distribution Network Operator (DNO) boundaries. Click on a region in the map below to see the current carbon intensity and generation mix for that region. Switch to the Country tab to see the same data for England, Scotland and Wales. Click the play button to see the forecast over the next 24 hours.
Enter your postcode into the box below to find out the carbon intensity of your region.
|#||Region||Forecast Carbon Intensity (gCO2/kWh)||Index|
The demand and generation by fuel type (gas, coal, wind, nuclear, solar etc.) for each region is forecast several days ahead at 30-min temporal resolution using an ensemble of state-of-the-art supervised Machine Learning (ML) regression models. An advanced model ensembling technique is used to blend the ML models to generate a new optimised meta-model. The forecasts are updated every 30 mins using a nowcasting technique to adjust the forecasts a short period ahead.
To estimate the carbon intensity of electricity consumed in each region, a reduced GB network model is used to calculate the power flows across the network. This considers the active and reactive power flows, system losses, and the impedance characteristics of the network. The carbon intensity of both active power flows (gCO2/kWh) and reactive power flows (gCO2/kVArh) is then calculated and the CO2 flows are attributed around the network for each 30 min period over the next several days. The carbon intensity of the power consumed in each region is then determined. The same approach is used to estimate the proportion of each fuel type consumed in each region.
A more detailed description of our methodology can be found below.
Our API Documentation contains instructions for developers to quickly integrate with our OpenAPI which is available at api.carbonintensity.org.uk. The API Documentation contains details about the different endpoints, return types, parameters and model schemas, as well as example code samples and responses.