Machine Learning Workshop 2024

RAVE Thematic Workshop on Machine Learning Applications for Offshore Wind Data Analysis

October 10th, 2024 10:00 - 16:00 (CEST)

Fraunhofer IWES (Am Fallturm 1, 28359 Bremen, Germany)

Hybrid event

Centered on the transformative power of machine learning in offshore wind data analysis, we aim to bring together researchers, data scientists and machine learning enthusiast at any career stage, to explore together the latest techniques, their applications using RAVE data and exchange expertise.

Anish Venu (DNVGL) will present current applications of machine learning methods in a Keynote: RAVE Machine Learning end-to-end cycle: A complete overview on the RAVE ML Model covering following topics:

1. 1. Selection of data
   2. Cleaning, manipulation and preparation of training data
   3. Development of ML algorithm and comparison of results from different algorithms
   4. Validation & performance testing of the algorithm
   5. Issues faced/identification of issues
   6. Sensitivity analysis of the model
   7. Transfer of model from one turbine to another
   8. Flagging strategies
   9. Final deployment
   10. Other applications
   11. Future works

The keynote will be supplemented by your contributions.  

The workshop is free of charge. To register rave-forschungsarchiv@bsh.de send an email to rave-forschungsarchiv@bsh.de until September 30th, 2024. Please indicate if you will attend in person or online.
We recommend early registration, as capacity on site is limited. Once registered, you will receive an e-mail including the guidelines for contributions, to be submitted until August 23rd, 2024. A final program will be distributed in late summer.

This is the link to dial in. Please klick on "Jetzt an der Besprechung teilnehmen" .  
Participation in the discussion is possible through the chat function. Online participation is anytime possible without a former registration.

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Jetzt an der Besprechung teilnehmen

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Datenschutz und Sicherheit

Join us for a collaborative event as we explore the power and different use cases of machine learning in the expanding field of wind energy! Please feel free to share this invitation with interested colleagues and fellow researchers to expand our community.

Call for contributions

While our keynote will focus on RAVE data, we are eager to open the floor to contributions from different topics and datasets on the offshore wind energy field. Whether you are working on predictive maintenance, anomaly detection, sensor data analysis, or other innovative applications of machine learning, we encourage you to share your insights, methodologies, and applications in machine learning. This is a unique opportunity to present your research, engage in meaningful discussions, and collaborate with peers.

Submission Guidelines

• Deadline for contributions: extended until August 31st, 2024

Please send us a short overview of your planned presentation to
rave-forschungsarchiv@bsh.de covering the following topics:

• Motivation and topic of your work
• Which dataset are you using? / Why RAVE data?
• Data preparation & Processing – Problems & Solutions
• Details on your ML algorithm and the theory behind it
• Results & Findings
• Future Work

ML Workshop Final Program (as of Sep 20, 2024)

All presentation times are CEST and include time for discussions and feedback from the audience.
Use of scheduled times is entirely up to the presenter.

10:00
Welcome and Introduction   Tanja Grießmann, Institute of Structural Analysis, Leibniz University Hannover

10:15
“RAVE Machine Learning end-to-end cycle: A complete overview on the RAVE ML Model“
Anish Venu, DNV Energy Systems

11:00 - 11:15 Coffee Break

11:15
“Localized Wind Profile Predictions via a Machine Learning Approach“
Farkhondeh (Hanie) Rouholahnejad, Fraunhofer Institute for Wind Energy Systems (IWES)

12:00 - 13:00 Lunch Break

13:00
“Neural Networks for Offshore Wind Turbine Converter Failure Prognosis“
Demitri Moros, Renewables Research & Development, EDF Energy UK & IDCORE

13:45 - 14:00 Coffee Break

14:00
“Application of a NARX-Based Surrogate Model for Offshore Wind Turbine
Structural Loads Prediction and Uncertainty Analysis“
Xu Ning, Geophysical Institute, University of Bergen

14:45 - 15:00 Coffee Break

15:00
“Assessment of a Deep Learning Surrogate Model for Wind Turbine Load Estimation Using RAVE Data“
Dexing Liu, Stuttgart Wind Energy (SWE), Institute of Aircraft Design, University of Stuttgart

15:30 Discussion, Networking and Wrap Up

Program download: here

In case you have any questions regarding the RAVE machine learning workshop, please contact us via rave-forschungsarchiv@bsh.de.