AI in the energy sector

Maggie Burns shares her views on how AI will affect the energy sector and the new guidance from OFGEM.

The integration of artificial intelligence (AI) in the energy sector is rapidly transforming the industry, offering opportunities to enhance operational efficiency, optimise resource management, and drive sustainability.

However, as this transformative technology becomes more and more essential to critical infrastructure, ensuring its safe and ethical use is a growing challenge.

To address these challenges, the Office of Gas and Electricity Markets (OFGEM) is drafting new guidance to promote the responsible and innovative use of AI in Great Britain’s energy sector.

Through an ongoing consultation process, OFGEM is inviting input from stakeholders – energy companies, AI developers, and experts – to shape a regulatory framework that balances innovation with safety and fairness (here).

The move to address these regulatory questions reflects a proactive approach to embracing a technology that, when applied responsibly, has vast potential to reshape the energy sector.

AI has already begun to play a transformative role in various aspects of the energy industry. For example:

• Smart Meters: AI is helping energy providers optimise consumption and improve grid stability by enabling real-time monitoring of energy use. Smart Meters collect vast amounts of data, which AI algorithms analyse to provide insights into energy usage patterns and predict demand spikes.  This allows for more efficient energy usage.
• Smart Grids: AI-driven smart grids allow for better distribution of electricity, ensuring that power is delivered efficiently to where it is needed. AI is effectively used in smart grids by analysing large amounts of data from sensors and smart meters to predict potential issues, optimise power distribution, manage demand response, and proactively prevent power outages.
• Predictive Maintenance: AI is also being used to predict when equipment is likely to fail or require maintenance. By leveraging machine learning algorithms to analyse large amounts of data collected from sensors and equipment, AI can identify patterns and signal potential failures in equipment before they occur. This approach allows energy companies to address issues before they escalate, reducing downtime, cutting maintenance costs, and ensuring the reliability of critical infrastructure.
• Customer Engagement and Energy Management: AI is increasingly being used to support consumers by providing personalized energy insights and automated energy-saving recommendations. Through AI-powered applications, customers can receive tailored advice on reducing consumption, optimising the use of home appliances, and even participating in demand response programs that offer financial incentives for reducing usage during peak periods. AI can also enhance customer service through chatbots and virtual assistants that help resolve billing inquiries, outage reports, and service requests efficiently.

These applications are just the tip of the iceberg and AI’s potential in the energy sector continues to grow.

One example of AI’s potential in the energy sector is its potential ability to reduce gate closure times – the period before energy delivery when market participants must finalise their trades – from 30 minutes to as little as 5-10 minutes.

By processing real-time market and grid data at unprecedented speeds, AI can enable more dynamic trading, improve supply and demand forecasting, and enhance grid flexibility.

However, as AI’s role expands, so too do the legal and regulatory challenges.

The use of AI in the energy sector is not without its legal complexities and several key considerations must be addressed to ensure it operates within established frameworks, protecting consumers, industry stakeholders and the public at large. Some of these key considerations include:

• Data Protection and Privacy: AI’s reliance on large volumes of data – often collected from consumers – raises significant concerns about privacy and data security. This raises important questions about consent and transparency in data usage. How can energy companies ensure that they are using data ethically while complying with data protection laws?
• Confidentiality: In addition to data protection, AI-driven systems in the energy sector must also address confidentiality concerns. AI models may process commercially sensitive information, proprietary business strategies, and operational data that must be safeguarded from competitors and unauthorised access. Legal frameworks will need to ensure that AI applications maintain strict confidentiality controls to prevent leaks or misuse of critical energy sector data.
• Intellectual Property: The development and deployment of AI in the energy sector raises significant intellectual property considerations. AI models, algorithms, and data-driven insights represent valuable assets that must be protected to encourage innovation. Legal frameworks must address questions regarding ownership of AI-generated data and decision-making processes, ensuring that developers, energy companies, and stakeholders receive fair recognition and protection for their contributions.
• Liability and Accountability: As AI assumes a more central role in decision-making processes, the issue of liability becomes increasingly important.  If an AI system fails, leading to an outage or a critical failure in energy infrastructure, who is held accountable? Is it the AI developer, the energy company that implemented the system, or both? These questions highlight the need for clear regulatory guidelines that address liability in the event of an AI error.
• Transparency: AI systems in energy must not function as “black boxes”, where decisions are made without transparency or the ability to understand the reasoning behind them. This is particularly important in an industry like energy, where AI decisions could affect public safety, pricing and fairness. Regulators, such as OFGEM, are likely to require that AI models be explainable, meaning that developers and operators must be able to demonstrate how their AI systems arrive at specific decisions. This could include explaining why an AI suggests adjusting energy prices, shifting energy loads, or shutting down certain power plants. By ensuring AI systems are transparent, stakeholders can trust that decisions are made responsibly and in line with regulatory principles.
• Complexity and Consumer Understanding: As AI increasingly influences decision-making in the energy sector, it introduces a layer of complexity that may be difficult for consumers to grasp.  AI can assist with market mechanisms, such as locational marginal pricing, which helps optimise grid efficiency but may make it harder for consumers to understand how energy costs are determined. The transition to algorithmic decision-making must be accompanied by clear communication strategies to ensure that consumers, regulators, and industry participants can follow the rationale behind AI-driven changes. Furthermore, as AI enables more real-time market adjustments, it is essential to balance automation with human oversight, ensuring that key decisions—such as grid stability measures—remain comprehensible and aligned with public expectations.  While AI can speed up processes like gate closure and market adjustments, it also adds complexity, requiring regulators and industry stakeholders to carefully manage this transition. The challenge is to make the system more efficient without making it so complicated that consumers and even some market participants struggle to keep up.

The consultation process being led by OFGEM is an opportunity for stakeholders—energy companies, AI developers, legal experts, and consumer advocates—to shape the future of AI in the energy sector.

Industry stakeholders are encouraged to actively engage in the consultation process to ensure that their concerns and insights are reflected in the final guidelines, which are set to be released in spring 2025.

By participating in the consultation, these groups can help create a regulatory framework that fosters innovation while taking into account important considerations like the ones discussed above.

Maggie Burns is a Junior Associate at Sharpe Pritchard LLP.

This article is for general awareness only and does not constitute legal or professional advice. The law may have changed since this page was first published. If you would like further advice and assistance in relation to any issue raised in this article, please contact us by telephone or email This email address is being protected from spambots. You need JavaScript enabled to view it..