10 AI-powered solutions announced for the Manchester Prize: Clean Energy Systems

Agent Net Zero is an innovative AI system that helps industrial companies become more sustainable by analyzing 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.

Agent Net Zero is led by the University of Sheffield and AMRC

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.

BiofuelAi is led by BiofuelAi, a University of Surrey Spin-out

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

Carbon Re is led by Carbon Re.

Every winter in the UK, people in low-income households suffer in cold homes because they can’t afford heating. GlowTrack by Horizon Zero aims to target support to those most in need by using secure transaction data with financial services, energy and property sectors to build a “heating support priority index”. To lower heating costs, this prototype will estimate total demand from the prioritised households in the index and with the aim of bulk-purchasing energy at wholesale rates, significantly reducing costs compared to retail rates. Pre-ordering ahead of winter would also provide suppliers with demand certainty, further driving down prices.

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.Optimiser-PhyX 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.

Deep.Optimiser-PhyX led by Deep.Meta.

DRIVE (Deep Reinforcement learning for Intelligent Vehicle and Energy optimisation) 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.

DRIVE is led by FPS (Flexible Power Systems).

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.

EnergyWall is led by Underheat, in partnership with University of Salford.

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.

Green Loops is led by University of Wolverhampton, in partnership with ABCircular GmbH.

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.

Grid Stability is led by University of Manchester.

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,” RaThPAs show where buildings are leaking heat and recommend 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.

Rapid Thermal Performance Assessment algorithms (RaThPAs) is led by Kestrix.