Hyperight

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.

Add comment

Highlight option

Turn on the "highlight" option for any widget, to get an alternative styling like this. You can change the colors for highlighted widgets in the theme options. See more examples below.

Instagram

Instagram has returned empty data. Please authorize your Instagram account in the plugin settings .

Ivana Kotorchevikj

Categories count color

Advertisement

Small ads

Flickr

  • Maria d'Odessa performs her art of make-up
  • Afro-deko-mono
  • Maria d'Odessa, touching
  • Maria d'Odessa au bâton de rouge-baiser
  • Maria d'Odessa & the red lipstick
  • Maria d'Odessa, soulful.
  • Peanuts
  • Celebrating the hundredth anniversary of Charles M. Schulz
  • À propos serendipity ...

Social Widget

Collaboratively harness market-driven processes whereas resource-leveling internal or "organic" sources.

ThemeForest