Meet the finalist teams of the Manchester Prize: Clean Energy Systems
www.nesta.org.uk/project-updates/meet-the-finalist-teams-of-the-manchester-prize-clean-energy-systems/
www.nesta.org.uk/project-updates/meet-the-finalist-teams-of-the-manchester-prize-clean-energy-systems/
The climate and nature crisis is one of the greatest long-term challenges we face in the UK and around the world. We need an ambitious approach to using artificial intelligence across the development, engineering and operation of our energy system to meet this demand.
The second Manchester Prize is incentivising the development and deployment of AI in this space to support the UK in realising its mission to be a clean energy superpower.
In January 2025, the Manchester prize opened and received 118 inspiring entries, all working towards developing AI tools and solutions that would reduce carbon emissions by 2030 and into the future.
Following a rigorous assessment and judging process, the 10 entries were selected by our expert judging panel.
“We are at a critical juncture in the journey to net zero, the next decade is make or break if the world is to keep global temperatures from exceeding 1.5C by 2050. Global emissions need to halve by 2030 compared to 1990 levels if we are to stay on track, while electricity production will need to double by 2050 to meet the demands of an electrified economy – clean energy innovation is essential. The rapid advancement of AI means we have tools like never before to achieve the goal of decarbonising the economy while supporting individuals, communities and businesses to thrive.”
Julia King, Baroness Brown of Cambridge, chair of the Manchester Prize judging panel
We are delighted to announce the 10 finalist teams of The Manchester Prize, Clean Energy Systems.
Agent Net Zero is an innovative AI system that helps industrial companies become more sustainable by analysing their environmental impact in real-time.
The system continuously monitors energy usage and emissions by connecting to various data sources across operations. Using advanced AI techniques, Agent Net Zero identifies environmental hotspots and automatically suggests practical improvements.
This gives businesses clear, actionable insights to reduce their carbon footprint while maintaining productivity and competitiveness, essentially providing a "sustainability assistant" that works 24/7 to help companies achieve their net-zero goals.
BiofuelAi brings cutting-edge AI and machine learning to the biofuel industry, optimising complex, variable processes in real time.
Traditional biogas production often relies on operator intuition due to unpredictable biological systems because biofuels are made from multiple material inputs. BiofuelAi solves this with advanced predictive models that create a digital twin of each site, enabling whole-system optimisation – from daily feedstock recipes to long-term acquisition strategies.
Developed by AI and sustainability experts, the platform boosts efficiency, profitability, and environmental impact, offering a scalable solution for cleaner, data-driven energy production worldwide.
Cement forms the foundation of our modern world but it has a sustainability problem - it is responsible for around 8% of global CO₂ emissions. Carbon Re is tackling this challenge by building AI process control software to cut emissions in cement production.
Acting like self-driving for industrial plants, Carbon Re optimises industrial processes in real-time, helping manufacturers cut both costs and carbon while transitioning to low-carbon operations.
A joint spin out of University College London and the University of Cambridge, Carbon Re was founded to deliver immediate climate impact for heavy industry.
Cavolo uses advanced AI to make city deliveries more efficient and eco-friendly.
The system helps businesses switch from traditional delivery vans to Light Electric Vehicles (LEVs), which are more efficient in busy cities. By using AI, Cavolo optimises delivery routes in real-time, reducing traffic, energy use, and emissions.
The technology helps make urban logistics faster and greener, allowing businesses to deliver goods quickly while saving time and reducing their environmental impact.
Deep.Meta is tackling carbon emissions in the steel industry with an AI-powered Digital Twin – a smart digital replica of the production process that combines physics and machine learning to optimise furnace operations.
By using real-time sensor data and material science, Deep.Meta more accurately predicts steel slab temperatures and improves scheduling, boosting energy efficiency and significantly cutting emissions. Unlike black-box AI, which can discourage adoption, Deep.Meta’s explainable, physics-based models offer clear reasoning, building trust with users.
Founded by experts in metallurgy and machine learning, Deep.Meta is already partnering with global steelmakers and aims to scale through broader industry collaboration.
Flexible Power Systems (FPS) helps big fleets like vans, trucks, and buses switch to electric by managing vehicles, chargers, and schedules with smart software.
FPS uses advanced AI called Deep Reinforcement Learning to solve complex, fast-changing problems – like where and when to charge – more quickly and efficiently. After training in a virtual world, the AI can make smart decisions in real time.
First used in EV fleets, this technology could also help with bigger energy challenges in the future.
In partnership with University of Salford, EnergyWall upgrades a building’s walls, gently warming or cooling homes from the outside, turning bricks into radiators that maintain a comfortable internal temperature all year round.
Using AI to analyse a building and off-site manufacturing, it designs and installs pipe systems into insulation panels for the walls of a building, making retrofitting buildings with heat pumps faster, cheaper, and less disruptive.
This approach is ideal for social housing, helping reduce carbon emissions, cut energy bills, and tackle condensation that causes mould. It’s a smarter, scalable way to decarbonise heating and fight fuel poverty across the UK.
In partnership with ABCircular GmbH, Green Loops tackles the challenge of recycling end-of-life photovoltaic (PV) cells by creating high-efficiency solar panels from recycled materials.
It uses machine learning to analyse the optical properties of materials and structures of solar cells. Using highly conductive artificially engineered MXene-based metamaterials, Green Loops optimises the design of solar cells to enhance energy performance while reducing manufacturing costs.
With the growing e-waste problem from old solar panels, the technology helps reduce waste, supports a circular economy, and makes solar energy more sustainable and accessible.
For electricity grids to function, there must be balance between the electricity going into the grid and the electricity leaving it.
Grid Stability uses AI and machine learning to quickly analyse power grid stability as more low-carbon technologies like wind, solar, EVs and heat pumps connect. It replaces slow, complex simulations with rapid, AI-driven assessments, enabling real-time monitoring, faster decision-making, and more confident planning.
This helps grid operators maintain reliability while scaling up clean energy solutions and cutting emissions.
Kestrix uses AI and thermal drones to map heat loss across entire neighbourhoods, acting as fast, 3D energy surveys from the sky.
This helps stakeholders like utilities, councils and housing providers plan energy upgrades with fewer costly, time-consuming site visits. Like a “Google Maps of heat loss,” the system shows where buildings are leaking heat and recommends fixes.
With a team of experts in computer vision and physics, Kestrix aims to speed up home retrofits, in turn cutting emissions, saving households money, and making homes warmer and healthier at scale.
Each finalist team is being supported with £100,000 in funding, up to £60,000 of compute and additional non-financial support to develop their solutions and put them in poll position to win £1 million in spring 2026.
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