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Manchester Prize to award £2 million to AI-breakthroughs that help make Britain a clean energy superpower

November 21, 2024
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The UK’s Department of Science, Innovation and Technology is calling on cutting-edge innovators to enter the second Manchester Prize. This year, the Manchester Prize is rewarding UK-led breakthroughs in artificial intelligence that will accelerate action towards the UK’s world-leading target to decarbonise the electricity grid to deliver green power by 2030 and reach net zero by 2050. 

Last year’s event focused on overcoming challenges in the fields of energy, environment and infrastructure (read about the ten finalists here).

Up to 10 of the most promising solutions will each be supported with £100,000 in seed funding, £60,000 in compute credits, and additional non-financial support to develop solutions capable of winning the £1 million grand prize in spring 2026. The winning solution will demonstrate not only technical innovation but also an evidenced road map to near-term (2030) adoption, scale and impact.

The Manchester Prize is a multi-million-pound challenge prize to reward UK-led breakthroughs in artificial intelligence for public good. Every year for a decade, it will reward innovations that will help to transform the lives of people across the UK and continue to secure the UK’s place as a global leader in innovation.

Minister for AI, Feryal Clark said:

“AI can transform our public services, make us more productive and tackle some of the biggest shared challenges in society. AI is already having a positive impact on so many aspects of our lives, but there’s much more waiting to be tapped into.

“The second round of the Manchester Prize will bring brilliant British innovation to bear to deliver a clean, secure energy future for the UK. Whether in energy, healthcare, or beyond, we’re backing AI innovations to deliver real and lasting change across the country.”

Chief Scientific Adviser at the Department for Energy Security and Net Zero, Professor Paul Monks said:

“The greatest long-term challenge we face is the climate and nature crisis: that’s why we have our world leading targets to decarbonise the electricity grid by 2030 and to reach net zero by 2050.

“We need an ambitious approach to using artificial intelligence across the development, engineering and operation of our energy systems and so I am pleased to see the Manchester Prize recognising that with its dedicated new round on decarbonisation.”

The Manchester Prize is encouraging teams of innovators, academics, scientists, engineers, start-ups and entrepreneurs to submit their solutions.

Entries to the second Manchester Prize should demonstrate a use of AI that accelerates the UK’s adoption of clean energy technologies at scale; enables efficient or low-cost operations of clean energy systems; and/or significantly reduces energy demand or optimises energy usage.

Applications of AI in clean energy systems could include increasing power outputs from wind and solar farms; co-ordinating the reliability of a decentralised energy network; predicting and integrating dynamic power supplies; increasing energy efficiency across industries and in our homes, and; increasing the resilience of existing infrastructure in a changing climate.

The Manchester Prize is funded by the Department for Science, Innovation and Technology and is being delivered by Challenge Works – a Nesta enterprise.

Tris Dyson, Managing Director, Challenge Works said:

“Science and technology makes its greatest advances when multiple ideas from diverse perspectives are applied to solving the same problem. Challenge prizes support open innovation with a level playing field for established and previously untested innovators alike, enabling the most promising ideas to progress with funding and expert capacity building support.”

Entries to the Manchester Prize must be UK-led, however, the teams can include innovators and partners from around the world. To find out more about the Manchester Prize and to enter before 1200 GMT on 17 January 2025, visit manchesterprize.org

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