The presentation will outline a comprehensive initiative aimed at leveraging generative AI to streamline the permitting process for clean energy projects. The primary objective will be to significantly reduce the cost and time required for licencing, which is currently a major bottleneck in the deployment of advanced nuclear and other clean energy technologies. The solution accelerator will utilise historical licencing data, real-time project-specific data, and various datasets to enhance the productivity of creating permits. It will integrate Azure Generative AI, Semantic Kernel orchestration, and Azure OpenAI Service, including GPT-4 and fine-tuned small language models, to automate document draughting and review processes. The system will assist regulators by quickly identifying missing information and ensuring compliance with regulations, thereby accelerating the approval process. Additionally, the solution will be extendable to multiple regulatory systems and languages, continuously adopting the latest advancements in generative AI to improve output quality.

Data centres currently account for 1-1.5% of global electricity consumption. As the demand for AI, data and cloud computing grows, it is crucial to address the need for energy-efficient practices and technologies to combat the challenge of high demand in energy consumption and cooling. While efforts are being made to decarbonise data centre operations, more collaboration and digital innovation are needed to make significant progress. Initiatives like the Net Zero Innovation Hub, in Denmark, are bringing stakeholders together to develop and implement solutions for sustainable data centres. Data-grounded and AI-powered capabilities have the potential to accelerate the energy transition for all, however, the question remains as to whether the benefits will justify the increased power demand.

Attendee insights:

Learn how data centres are future-proofing their operations through decarbonisation and what steps are being taken to help mitigate the industry's environmental impact.