Applying data science to operations to improve profitability

When: Tuesday, 25 April 2023, 9:00 - 10:30
Where: Auditorium 15

Session description

Data science methods are able to automate large-scale data analysis and thus reduce costs and risks of the wind farm lifecycle. During this session, various data science applications are shown for wind turbine condition and performance monitoring. Furthermore, open-source and data sharing methods that enhance collaboration and drive innovation within the wind industry are discussed. The topics presented can be applied to enhance automation, increase profitability and improve safety in the wind energy sector.

Session chair

Jan Helsen

Professor, Vrije Universiteit Brussel

Sofia Koukoura

Senior Asset Performance Analysis Engineer, ScottishPower Renewables

Presentations

Pattern mining based data fusion for wind turbine condition monitoring
[scientific paper submitted - IOP Journal of Physics: Conference Series (volume 2507)]

Xavier Chesterman

PhD researcher, Vrije Universiteit Brussel

Data-driven characterization of performance trends in ageing wind turbines
[scientific paper submitted - IOP Journal of Physics: Conference Series (volume 2507]

Alessandro Murgia

Data Scientist, Sirris

Open-source wind turbine health predictor on the OSDUTM data platform

Hayley Horn

Sr. Solutions Architect, Databricks

Overcoming the barriers to digitalisation in wind energy - two practical examples and learning experiences
[scientific paper submitted - IOP Journal of Physics: Conference Series (volume 2507)]

Sarah Barber

Head of Wind Energy Innovation Division, Eastern Switzerland University of Applied Sciences

Closing fire-side chat

Sofia Koukoura

Senior Asset Performance Analysis Engineer, ScottishPower Renewables

Scott Sanderson

Director Global Business Development, Energy, Amazon Web Services (AWS)