EU Energy Projects Podcast: The potential of AI and GenAI in Europe with ETIP SNET

The ETIP SNET AI white paper explores the transformative potential of AI and generative AI in Europe's smart grids, incorporating industry insights to identify key uncertainties, challenges and opportunities.

Designed as a roadmap for stakeholders, the paper aims to unlock AI and GenAI’s potential in Europe’s energy transition. It is being developed with extensive stakeholder input to present a unified perspective on these evolving technologies.

To dive into this complex issue, I sat down with Luis Cunha, chairman of ETIP SNET, during the Enlit Impact Circle in Bilbao.

AI as a game-changer for energy systems

Cunha emphasised that AI is not just a tool but a fundamental enabler of the energy transition. "Artificial Intelligence can help us manage the complexity of modern energy systems in ways that were previously unimaginable," he explained.

With increasing renewable energy penetration, decentralised grids and fluctuating demand patterns, AI offers solutions to enhance grid stability and efficiency.

The ETIP SNET AI white paper outlines key areas where AI can provide value, including demand forecasting, predictive maintenance and grid optimisation. By leveraging machine learning algorithms, energy operators can better predict consumption patterns, reducing waste and enhancing sustainability.

Overcoming challenges: Regulation, data and trust

Despite AI’s potential, Cunha pointed out several challenges that must be addressed for widespread adoption. "The biggest hurdle is ensuring that AI systems are transparent, secure and aligned with regulatory frameworks," he stated.

The white paper discusses the need for standardised data sharing protocols and regulatory guidelines to ensure AI-driven decisions remain fair and unbiased.

Another challenge is the availability and quality of data. AI models require vast amounts of data to function effectively, but accessing and standardising energy-related data across different stakeholders remains a significant obstacle.

The white paper calls for collaboration among policymakers, energy companies and AI developers to create a common framework for data exchange.

Also of interest

AI in smart grids – shaping a roadmap for Europe

Real-world applications of AI in energy

Cunha highlighted some promising real-world applications of AI in the energy sector. "We are already seeing AI being used in predictive maintenance, reducing costs and preventing outages before they happen," he noted.

By analysing sensor data from energy infrastructure, AI can detect potential faults early, improving reliability and reducing operational expenses.

Additionally, AI-driven demand response systems are helping balance supply and demand more efficiently. Smart algorithms can adjust energy distribution in real time, minimising waste and ensuring renewable sources are used optimally.

The future of AI in the energy transition

Looking ahead, the ETIP SNET AI white paper envisions a future where AI plays an integral role in a decentralised and digitalised energy landscape. Cunha stressed the importance of continued research and collaboration: "We need to foster innovation while ensuring AI remains ethical and aligned with our broader energy transition goals."

To fully unlock AI’s potential, policymakers, technology providers and energy stakeholders must work together to create a regulatory environment that supports AI-driven innovation while safeguarding consumer interests.

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