Deep Learning for enhancement of earth observational data – Hjalte V. Kiefer, Vestas Wind Systems

The renewable energy industry has only recently started to rely on data-driven models on applications that have traditionally required complex physical solutions.

In this talk, we would like to show how we leverage from Spark, Keras and (in our case, on-prem) high performance computing (HPC) infrastructure to tackle a common and interesting problem in the wind-related industry (saving hours of CPU-consuming simulations).

The whole process is semi-automated at the moment of writing.

We are working on submitting a patent application for this work, and so we will be happy to provide more details regarding the use case and evaluation results very soon if required.

Key Takeaways

  • Inspiration on how to use deep learning for this and similar use cases.
  • Lessons learnt (e.g. limitations we have seen of using Hive, possibilities of data representation,…).
  • Tailored-made infrastructure to learn hyper parameters of deep neural networks.
  • Evaluation results.

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