Posters
Come meet the poster presenters to ask them questions and discuss their work
We would like to invite you to come and see the posters at our upcoming conference. The posters will showcase a diverse range of research topics, and will give delegates an opportunity to engage with the authors and learn more about their work. Whether you are a seasoned researcher or simply curious about the latest developments in your field, we believe that the posters will offer something of interest to everyone. So please join us at the conference and take advantage of this opportunity to learn and engage with your peers in industry and the academic community.
PO005: WindOpsBench: Benchmarking Large Language Models for Operational Data Analytics in Wind Energy
Michael Tinning, Co-Founder & Head of Technology, Bitbloom
Abstract
The capabilities of Large Language Models (LLMs) are increasing dramatically, and are now starting to be adopted in wind energy operations and maintenance. In order to achieve widespread adoption, high confidence in their outputs is paramount and therefore it is crucial to understand their performance within the domain. Despite many benchmarks being available across a range of sectors, until now none have been available within the domain of operational data analytics. Bitbloom presents WindOpsBench, a new benchmark for measuring the effectiveness of LLMs at solving problems commonly faced by operational data analysts within the wind energy domain. WindOpsBench analyses the performance of leading LLMs across a range of common, high-value workflows including: identifying underperforming assets; suggesting remedial action or further inspection requirements; identifying component health issues early; clearly communicating evidence backed by root-cause analysis; identifying opportunity values; and summarising complex information for a range of stakeholders. Mature benchmarks for other industries have helped to drive confident AI adoption by providing adopters with a stronger understanding of the opportunities and risks of AI (for example, HealthBench/MedPerf in healthcare, LawBench/LegalBench in law, SWE-bench Verified in software engineering). By introducing WindOpsBench for the Wind Energy O&M domain, we help the Wind Energy industry understand the strengths, weaknesses, and opportunities of the leading LLMs and offer a valuable resource to accelerate AI adoption.
No recording available for this poster.
