AI: a cross-lifecycle tool for net zero
AtkinsRéalis’ Data and Artificial Intelligence Practice Director, Anna Lewis, considers AI’s potential contribution to net zero solutions across their lifecycle.
Artificial Intelligence holds many keys, and this makes it a great foil for net zero, which has many challenges, all with rather tricky locks. Generally, we are aware of what needs to be done in order to mitigate the climate crisis, or at least the big picture goals. We want to use less energy, produce less greenhouse gas, and facilitate carbon sinks to help us in soaking up what we’ve already produced. However, the detail of these goals is far more difficult to consider: just listen to any mainstream talk radio programme about the problems and you’ll find a hundred answers and seemingly a thousand new problems. AI, thanks to its exponential bandwidth for data assessment and analytical insight, can help to vastly narrow net zero uncertainty, delivering clear exits from our human decision paralysis.
Enhanced strategic visibility
The first area where AI is already being leveraged is at the strategic stage, when multiple strands of options and their pros and cons need to be brought together, to deliver a holistic plan that answers the needs of as many stakeholders as possible. For example, looking at the energy landscape at a national level, with generation, demand, existing network infrastructure, and potential changes in the future based on both climate change impact and demographic shift. To provide actionable direction that’s a lot of data to crunch, involving a wide range of specialist professionals, as well as members of the public who are, of course, experts in where they live, work and play.
AI can help us manage and analyse these data flows in numerous ways. For example, by leveraging Large Language Models to identify contributors, trawl through relevant policy across multiple organisations, and even navigate through comments from the public to define the problem, or identify sentiment for or against particular ideas. This can help communications planning to provide additional reassurance if needed, or if AI readiness is assessed as low, extending implementation timeframes to accommodate.
Broader optioneering
Optioneering generally happens at a lower level to strategy, perhaps examining net zero strategies for a region, single network, or even a town or village. AI professionals may leverage AI enabled simulation and predictive analytics to consider various options against future risk profiles. When this is completed without AI intervention the data inputs are more limited, due to the bandwidth of the human assessor who must both identify sources and understand the outputs. AI enhances human understanding, facilitating more accurate and granular decision making, rather like replacing reading glasses with the Hubble telescope.
A key tool for this area is the genetic algorithm, which mimics natural selection to create and evaluate potential solution scenarios. For any net zero professional, our exploitation of nature-mimicry in becoming part of our solution for climatic recovery is perhaps a neat jigsaw piece in itself. Within a genetic algorithm, a set of constraints is introduced by the AI professional, for example the energy demand required within an area, or the engineering standards for twists and turns within a utility network, and a random population of solutions is introduced. The algorithm selects the solutions which provide the most satisfactory result against the constraints, then begins to blend these ‘best-fit’ options to produce even better results, introducing random ‘mutations’ to test whether these further optimise the solution, and iterate until a really strong set of options is produced. This process can be almost instantaneous: with a similar algorithm used to design utility networks, our team at AtkinsRéalis produced 10,000 prioritised options in the time it previously took a manual process to produce just one.
Given net zero’s complexity, this optioneering power is particularly appreciated. For example, introducing options where reuse of existing infrastructure or components is required in order to reduce waste and cut carbon from the development process, rather than ‘easier’ greenfield designs where everything is created from scratch with far fewer complications of design adaptation.
Construction management and delivery
Once a design has been selected, there’s then the task of creating it in reality to see its net zero potential come to life, and speed is of the essence! Here, we enter the programme design element of artificial intelligence, as well as numerous very exciting and highly specialist construction applications. Programme elements can be designed to work together in an optimiser fashion very similar to the designed asset itself, predicting risk, minimising downtime between components, and achieving cost and time efficiencies. With finance always a factor in net zero debates, AI is a key tool to unequivocally demonstrate cost effectiveness.
Predictive operational maintenance
Once up and running, the key for net zero as for any other area, is to keep running assets in play. Particularly where projects have used AI and its associated data from strategic conception, operational maintenance is also vastly improved. Availability of assets hinges both on the individual assets’ operation, and the capacity of their connected infrastructure. For example, if a wind turbine develops a fault for any reason, the energy from that turbine will be lost. However, if the grid connection from the array goes down, then the energy across the whole array will be lost. AI can track operational data and examine historic data from both the asset network and from other asset networks, to spot these kinds of problems before they become critical, and can design intervention programmes of maintenance which will keep everything running smoothly.
AI – a full-lifecycle tool towards net zero
The power of AI across the spectrum of net zero interventions, and its ability to support an asset or asset network across its lifecycle, should place it firmly at the heart of our net zero toolbox. However, it’s critical that we don’t envisage it as a tool that sits in isolation, a kind of silver bullet which invalidates all our previous techniques. For example, in our optioneering example, where a genetic algorithm produces 10,000 prioritised options for a utility network design, this doesn’t mean we remove the need for appraisal by the utility professional. It just means that professional has enhanced their insight capacity, by focusing on a smaller pre-selected group of options without spending nugatory effort on bad-fit solutions. Any engagement with AI by a net zero professional should be with the goal of a mutual building of capacity and intelligence, and through that augmentation (rather than replacement) we can truly achieve a gear change towards net zero.
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Usman Ikhlaq
Usman Ikhlaq
Usman joined techUK in January 2024 as Programme Manager for Artificial Intelligence.
His role is to help techUK members of all sizes and across all sectors to adopt AI at scale. This includes identifying the barriers to adoption, considering solutions and how best to maximise AI's potential.
Prior to joining techUK, Usman worked as a policy, government affairs and public affairs professional in the advertising sector. He has also worked in sales and marketing and FinTech.
Usman is a graduate of the London School of Economics and Political Science (MSc), BPP Law School (GDL and LLB) and Queen Mary University of London (BA).
When he isn’t working, Usman enjoys spending time with his family and friends. He also has a keen interest in running, reading and travelling.
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Authors
Anna Lewis
Anna Lewis, a Chartered Management Consultant, is the Director of the Data and Artificial Intelligence Practice in the Aerospace, Defence, Security, and Technology division of AtkinsRéalis. She specialises in AI strategy and has led multi-disciplinary teams to achieve strategic, enterprise-wide AI transformation for both large public and private sector clients in complex environments.