AI in the energy sector – growing energy demand but potential to transform it

The wide range of potential impacts of AI in the energy sector, both its growing energy consumption and its ability to transform the sector, are the focus of a new report from the IEA.

The challenge for AI is essentially two-fold – one its fast growing energy consumption as data centres burgeon across the world and the second its ability not only to contain this growing consumption but also to play a transformative role in how energy systems are managed as they become increasingly complex.

The new report from the IEA, ‘Energy and AI‘ drawing on new datasets and input from stakeholders, addresses both these areas.

Coincidentally it also appears at the same time as a position paper from Europe’s ETIP SNET setting out a vision and roadmap for its advance, particularly in Europe’s energy sector.

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AI and data centres

The report comments that global investment in data centres has nearly doubled since 2022, which has driven the concerns about the growing electricity demand.

For example, a typical AI-focused data centre consumes as much electricity as 100,000 households, while the largest ones under construction today will consume 20 times as much.

The IEA projects that electricity demand from data centres worldwide is set to more than double to around 945TWh by 2030 – slightly more than the entire electricity consumption of Japan today. AI is the most significant driver of this increase, with electricity demand from AI-optimised data centres projected to more than quadruple by 2030.

The US, currently accounting for almost half of global data centre electricity consumption, is expected to account for largest share of the projected growth, followed by China, which currently accounts for about a quarter of the consumption, and then Europe currently on 15%.

With that the US is set to consume more electricity for data centres than for the production of aluminium, steel, cement, chemicals and all other energy intensive goods combined, according to the IEA.

The IEA anticipates that renewables supported by storage and natural gas will take the lead in meeting data centre electricity demand, but other sources, such as nuclear with the first small modular reactors coming online around 2030 and geothermal also to contribute.

However, a challenge is that electricity grids are coming under increasing strain and grid connection queues for both supply and consumption projects, including data centres, are long and complex.

If not addressed, up to a fifth of planned data centre projects could be at risk of delays, the IEA comments.

The IEA also notes that the significance of data centres in driving electricity demand varies by country and cautions that there are uncertainties in how quickly AI will be adopted, how capable and productive it will become and how fast efficiency improvements will occur, giving rise to large uncertainty in the outlook for AI-related electricity demand.

AI and the energy sector

The report points out that AI is already being deployed by energy companies to transform and optimise energy and mineral supply, electricity generation and transmission and energy consumption.

Drawing on input from utilities, the broad areas where AI applications are in use are in real-time operation, asset operation and maintenance and grid development.

Specific use cases highlighted include the forecasting and integration of variable renewable energy generation, reducing curtailment and emissions, fault detection and network management.

For example, AI-based fault detection to rapidly identify and precisely pinpoint grid faults can reduce outage durations by 30-50%. Remote sensors and AI-based management can increase the capacity of transmission lines, with up to 175GW of transmission capacity potentially able to be unlocked if these tools are applied, without any new lines being built.

Another area for AI is its integration into manufacturing, with applications able to accelerate product development, lower costs and increase quality.

In transport AI applications are being used to manage traffic, optimise routes, predict maintenance needs and develop autonomous vehicles.

In buildings, there is significant potential for AI-led optimisations to make heating and cooling systems more efficient and electricity use in buildings more flexible.

Collaboration

Nevertheless the energy sector is not yet making the most of AI, according to the IEA.

Energy is amongst the most complex and critical sectors in the world today but faces barriers to realising the widespread adoption of AI, including missing or inadequate access to data and digital infrastructure and skills, as well as persistent digital and physical security concerns.

For example, the prevalence of AI-related skills is much lower in the energy sector compared with other sectors.

With energy and tech now on a journey together, collaboration is key, the IEA states in its conclusions.

The tech sector and energy industry are more intertwined than ever before. There are large uncertainties on the path ahead, but these should not get in the way of concerted action.

Delivering the energy for AI, and seizing the benefits of AI for energy, will require even deeper dialogue and collaboration between the tech sector and the energy industry, the IEA says in the report.

“With the rise of AI, the energy sector is at the forefront of one of the most important technological revolutions of our time,” says IEA Executive Director, Dr Fatih Birol.

“AI is a tool, potentially an incredibly powerful one, but it is up to us – our societies, governments and companies – how we use it.”

As part of its activities towards this, the IEA intends to soon launch a new observatory on energy, AI and data centres, which will gather data worldwide on AI’s electricity needs and track cutting edge AI applications across the energy sector.