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School of Mathematical Sciences

Crowdsourcing AI expertise to inform our pathways to net zero

Climate change is one of the biggest issues we are facing and Professor of Mathematics, John Moriarty, is exploring how AI can be used to determine the best routes to net zero emissions. 


Aside from their work at Queen Mary, many of our academic staff are involved in projects outside of the University. Professor John Moriarty, who specialises in applied probability and applications in energy, has been working on an exciting project with the Alan Turing institute and the Net Zero Technology Centre.

His current work explores how artificial intelligence (AI) can be used to determine the best route to our goal of net zero emissions. As a part of this project, John and his team developed RangL, a competition platform which provides a new model of collaboration between academia and industry. RangL was designed to be a user-friendly environment, to which anyone can contribute, with the aim of developing learning approaches to data-driven control problems. 

We spoke to John to learn a bit more about RangL and how the competition stage went.

Where did the idea of a competition platform come from? 
I helped organise the 2019 Mathematics of Energy Systems research programme at the Isaac Newton Institute (INI) in Cambridge. During one of the INI workshops, participants from industry suggested the creation of a set of benchmark problems for future energy systems on which a range of solutions could be tried and compared. This idea naturally morphed into the creation of a competition platform based around energy system problems. 

And how was the response?
Very encouraging! Over 150 people signed up for the platform, from the UK, Europe, US, India, Hong Kong, China, Mexico, Brazil and Australia. 

Were there any submissions that really impressed you?
Although not quite top of the leader board based on numerical score alone, a team from the Austrian Institute of Technology and University of Natural Resources and Life Sciences in Vienna made a submission using a state-of-the art reinforcement learning algorithm which we hope could be useful to the challenge's industrial sponsors, the Net Zero Technology Centre. Because of this, we awarded the team joint first place.

To learn more about RangL, you can read an article that John and his colleagues recently had published in Science Direct: Reinforcing the role of competition platforms.

The Net Zero Technology Centre is holding a webinar titled AI informing optimal pathways to net zero on 28 March 2022 and John will be presenting on the RangL competition. To register for the event, please visit the Net Zero Technology Centre Website.



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