DESNZ launches consultation on data for AI in the energy system

The Department for Energy and Net Zero has published a consultation on data for AI in the energy system. The consultation closes 24 April 2026.

The potential of AI 

Data is one of the UK’s unrealised growth assets and it is estimated that effectively harnessing private and public sector data has the potential to increase productivity growth by 0.5-1.3% per year. The government is aiming to enable to the use of high-quality data across the public and private sectors, extend smart data initiatives, and establish a clear framework to the value and licence public sector data assets.

DESNZ considers that Artificial Intelligence (AI) has significant potential to make the global energy system more efficient, thereby reducing carbon emissions and making it cheaper to operate. Electricity systems are becoming more complex, and power is generated from a wider and more dispersed range of sources. Increases in smart appliances and flexible demand will require data and exchange tools that can forecast, model, plan, automate and execute actions quickly without driving up costs. The International Energy Agency suggests that the range of AI applications in the sector may include:

• Balancing electricity networks that are growing more complex, decentralised and digitalised.
• Improving the forecasting and integration of variable renewable energy generation, reducing curtailment.
• Supporting electricity grid operations to enable better fault detection that can help rapidly identify and precisely pinpoint grid faults, reducing outage durations by 30-50%.

The consultation 

The consultation is aimed at AI developers and is intended to gather evidence to improve understanding of which high impact datasets could support the development of key AI applications in the energy sector, as well as identifying barriers to accessing data and developing AI applications in the energy system. Data is critical for developing AI applications in the energy sector. Data not included in the training sets of current models or which encodes new insights about the world is particularly valuable. Responses to the consultation will directly inform the upcoming AI for the energy system strategy.

The consultation’s defines AI as “a machine-based system that, for explicit or implicit objectives, infers, from the input it receives how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.” This broad definition has been selected to include a wide range of processes in the scope of the consultation, such as generative AI, deep learning and computer vision. 

The consultation’s areas of focus are:

• System energy data: this includes data on energy infrastructure and generation, such as component energy data (such as performance of turbines or transformers), capacity, energy forecasting, network infrastructure, renewables data, and hydrogen and heat data; as well as market data such as energy consumption and prices, energy efficiency, smart meter data, data on market settlement and imbalances, and policy and regulation,
• Consumer energy data: including energy supplier held data, traditional meter data, low carbon technologies, energy behaviour data, and consumer profiles.
• Data sets with indirect importance to energy, such as weather data sets, mapping data, or electric vehicle data.
• Understanding the data or access barriers that might prevent developing AI applications in the energy sector. This might incomplete or insufficient data, data being fragmented across organisations, systems or formats, ownership of proprietary data, costs associated with commercially available data, and access restrictions due to privacy concerns, security risks, legislation or risk policies.

Consultation questions

1. Define the problem

The consultation is asking developers to identify a problem that bottlenecks achieving clean energy, such as decarbonisation, energy security or affordability and explain how AI could help solve this.

2. Define the data

Respondents should set out what data exists, in what form, where it is stored, and who owns it. 

3. Steps to creating a useable data set

Developers should consider the level of work required to the dataset available and practical, including if there are any reasons why the identified dataset has not been created already. 

4. Other data users

Respondents are asked to consider who else should have access to the data and who might benefit, as well as considering any unintended risks or consequences that might need to be managed.

5. Scale of data required

Respondents are asked to consider the cost vs utility trade off of enabling access to the identified data at scale, and whether a smaller or sample data set could be sufficient. 

6. How will this data set help?

Perhaps most importantly, developers will need to explain the scientific, technological and translation impact of this AI work; how will solve both the identified problem and the benefits to UK energy in general, with reference to measurable outcomes, such as lower emissions or reduced bills.

7. Arrangements for ongoing maintenance, governance and curation of the data set

Respondents should consider whether the data set can be incorporated into existing databases or if it would require its own infrastructure, as well as what controls will be.

If you would like to discuss AI, please contact Tom Whittaker, Brian Wong, Lucy Pegler, Martin Cook, Liz Griffiths or any other member in our Technology team.  For the latest on AI law and regulation, see our blog and newsletter.

This article was written by Harriette Alcock and Tom Whittaker.