Photo of Lyndon Ruff - used for the National Grid story 'Lyndon Ruff: making a difference to carbon intensity via data'

Lyndon Ruff: making a difference to carbon intensity via data

Lyndon Ruff, 29, works as Lead Data Scientist at National Grid’s Electricity System Operator. He uses machine learning to predict how the Great British weather will impact solar and wind power production. He’s also part of the team that created carbonintensity.org.uk, allowing consumers and businesses to see forecasts of how carbon dense the UK’s energy generation will be.

 
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Lowering emissions with machine learning

Solving real world challenges that make social impact is what brings me to work every morning. Britain’s Net Zero policy excites me and I am in a position to significantly contribute to operating a carbon free National Grid by 2025.

At National Grid ESO we’re pushing the boundaries of machine learning and deep learning, collaborating with some of the sharpest minds to reduce the carbon intensity of Britain’s energy system.

Britain’s Net Zero policy excites me and I am in a position to significantly contribute to operating a carbon free National Grid by 2025.

Predicting the great British weather’s impact on generation

The country needs as much clean energy as we can produce from sources like solar and wind power. This comes with added complexity, as these are driven by weather – which, as we all know, is not always the easiest to predict in the UK!

While we can’t change the weather, we do have the potential to get much better at accurately predicting how sun, wind and cloud formations will impact on renewable generating capacities. This is my job. We use state-of-the-art deep learning techniques to give us information in increasingly precise ways. This allows us to operate the grid with less carbon being emitted by the generation fleet.

The more accurately we plan, the less carbon intensive our energy generation becomes.

Smarter energy demand forecasting

Whether our renewable capacities are running high or low, smart forecasting is important; because it helps us plan how much extra generating capacity we need to fulfil the country’s demand for electricity. Often this stand-by generation comes from less clean sources like gas and coal. Since this stand-by generation needs to be ready to start-up quickly, they’re often kept idling. Like a car engine that’s kept running while stationary they produce carbon. So, the more accurately we plan, the less carbon intensive our energy generation becomes.

Data scientists are hot property

There’s a big demand for data science experts in many industries. Within National Grid ESO, I have the opportunity to solve real-world challenges every day. Knowing that the work I do is making a difference and influencing change is a huge motivator.

A world first project

We’re open in sharing our knowledge and I get a great buzz from this too. Our online carbon intensity project – created with the Environmental Defense Fund Europe, the World Wildlife Fund and Oxford University – is a world first. Users see forecasts of how carbon dense the UK’s energy generation will be up to 96 hours in advance – you can even view real time regional differences.

Hitting the millions

The site’s really popular and we get 5 million hits a month, from people at home wanting to know when’s the best time to switch on their washing machine, to green energy companies looking for a heads-up on renewable outputs. Samsung has even made it part of their in-schools learning project, teaching pupils how to code on MicroBits using our carbon intensity data. That’s what I call a real perk of the job – having a direct impact on the next generation’s knowledge and actions.

 

See Lyndon and his colleagues’ work