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.
On 9 April at 17:15, we’ll also hold the main poster session and distinguish the 7 best posters of this year’s edition with our traditional Poster Awards Ceremony. Join us at the poster area to cheer and meet the laureates, and enjoy some drinks with all poster presenters!
We look forward to seeing you there!
PO200: Automating Insights and Actions in Renewable Energy Maintenance with Machine Learning
Silvio Rodrigues, Co-founder and CIO, Jungle AI
Abstract
As renewable energy capacity expands, asset managers face the challenge of managing increasingly complex machines across larger and more diverse portfolios. This complexity is compounded by the growing volume of data generated by these assets, leading to a flood of alarms that can overwhelm technical teams. The difficulty prioritising issues among many competing signals hampers efficient decision-making, leading to increased operational costs and reduced energy production. There is a pressing need for automated solutions that not only model the normal behaviour of machines but also streamline the interpretation and prioritisation of anomaly detections. For the past 6 years, data scientists from Portugal have been researching how to leverage machine learning models to establish a baseline of normal machine behaviour. By integrating wind turbine domain expertise with the latest developments in data science, they developed a system that identifies relevant events and deviations from expected performance across all assets. Building on this, ChatGPT automates the entire process, acting as an intelligent translation layer that interprets these detections within the context of the machine's operations. Automated descriptions of detected issues are generated, recommending appropriate next actions, while prioritising and labelling issues based on severity levels. Additionally, ChatGPT automates the creation of tailored report templates and searches for similar relevant historical cases to provide context to the user. These automated reports are sent directly to service engineers, enabling quicker and more informed responses. Aiming to empower the people behind the machines, this idea combines automated tools with the power of machine learning and domain expertise. Asset management teams are relieved from the burden of interpreting and prioritising machine alarms while advancing the goal of increasing renewable energy efficiency. By automating the detection and addressing of issues, this application enables lower operational costs, increased energy production, and the extended lifespan of renewable assets.
No recording available for this poster.
