Call for Participation: Workshop on AI for Energy Networks, Bath, 16 Jan 2026

[Özgür Şimşek]

Call for Participation: AI for Energy Networks Workshop

Friday, 16 January 2026, University of Bath, UK

We invite leading AI and Machine Learning researchers and practitioners to join experts in energy networks for a focused, collaborative workshop on the opportunities and challenges of AI applications in energy networks.

The transition to net zero is driving a rapid transformation of the energy sector, driven by renewable generation, low carbon technologies, and the rise of data and digitalisation. This transformation comes with emerging opportunities and threats. AI technologies have the potential to enhance grid resilience, optimise asset management, and improve forecasting accuracy, amongst many others. This is a unique opportunity to learn about the challenges facing energy networks—especially at the national and regional scales where many actors interact—and to apply your technical expertise to a critical piece of national infrastructure.

Energy networks play a vital role in our energy systems, transporting energy in both time and space from production to consumption. Examples of energy networks include electrical power transmission and distribution networks, gas transmission and distribution networks, and heat networks.

This workshop is a working session with mutual knowledge sharing and problem-solving; it is not a conference. You will learn about the challenges facing energy networks and collectively identify high-impact problems with immediate potential, motivating future AI research and deployment.

The intended outcome of this workshop is a position paper summarising the group’s recommendations and setting a research agenda for AI in energy networks. The paper is intended for submission to a peer-reviewed venue. 

The workshop will take place on Friday, 16 January 2026, in Bath, UK, and is in-person only.

Who Should Apply?

AI researchers and practitioners with expertise in a broad range of AI disciplines with potential to address critical energy challenges, including prediction, optimisation, and decision-making under uncertainty. Examples include, but are not limited:

• Generative AI
• Reinforcement Learning 
• Deep Learning
• Graph Neural Networks
• Natural Language Processing
• Computer Vision
• State estimation
• Anomaly Detection
• Time-Series Forecasting
• Causal AI
• Explainable AI

No prior knowledge or experience in the energy sector is required.

Please apply here: Call for Participation - AI for Energy Networks Workshop

We encourage you to apply by Friday 19th December 2025. Applications will be reviewed on a rolling basis.

Enquiries to tms...@bath.ac.uk 

Many thanks.

Thomas Smith, Furong Li, Özgür Şimşek