As part of the TWEED European Doctoral Network activities, several doctoral candidates recently participated in an advanced training course held in Denmark focused on Data Science applications for Wind Energy.
The programme combined theoretical lectures and practical hands-on activities addressing key topics related to the digitalization of wind energy systems. Among the subjects covered were FAIR open-science data principles, probability and statistics for data science, data visualization and filtering techniques, feature selection, and research integrity in data science.
Participants also explored the application of machine learning and deep learning methods for wind energy, including software tools and real industrial and research perspectives presented by experts from academia and industry.
A central component of the activity was a three-day hackathon, during which the researchers worked collaboratively on real case studies related to:
- data curation from LiDAR measurement campaigns,
- wind turbine load prediction,
- wind farm power output forecasting,
- and fault prediction based on normal behaviour modelling.
The course provided an excellent opportunity for the doctoral candidates to strengthen both their technical competencies and their collaboration within the international and multidisciplinary TWEED network.
Activities such as this reflect the commitment of the TWEED project to promoting high-quality research training, interdisciplinary collaboration, and innovation in the field of wind energy digitalization within the framework of the Horizon Europe programme.
