GenAI in the energy sector: Guidance and Experience

The energy sector is currently facing three significant changes: decarbonization, decentralization, and digitization. These changes are transforming the way energy companies operate. To navigate these changes in an increasingly complex environment, businesses are adopting digital technologies. At the same time, there is a need for the workforce to upskill and align with the new digital paradigm. Generative Artificial Intelligence (GenAI) has the potential to assist companies in the energy sector throughout the entire energy value chain, including production, distribution, markets, and consumption. Our study examines the use of GenAI in the energy sector by: (1) evaluating different GenAI techniques, such as Generative Adversarial Networks (GANs), (Variational) Autoencoders, Diffusion Models, and Large Language Models; and (2) conducting a case study analysis for TenneT, a leading European electricity transmission operator. The study presents a comprehensive review of these methods and a technology map to help understand their interactions. Based on this, we explore specific GenAI applications for TenneT, carefully selected to demonstrate their potential for impactful use in the energy sector. We evaluate each use case against a set of specially developed criteria. These include technological maturity, potential for value creation, data accessibility, and regulatory compliance. We conclude by providing specific recommendations for action, enabling stakeholders in the energy sector to make effective use of GenAI using the paradigms identified by us. Attendees will leave equipped with a clear understanding of how GenAI can help the energy sector tackle their challenges and how to effectively activate its potential.