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On-Prem Solution for the Selection of Wind Energy Models
On-Prem Solution for the Selection of Wind Energy Models

On-Prem Solution for the Selection of Wind Energy Models

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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 Spark, Keras and (in our case, on-prem) high performance computing (HPC) infrastructure to potentially tackle common and interesting problems in the wind-related industry (saving hours of CPU-consuming simulations).

We use:

Apache Spark and Hive for data preparation and a combination of different data sources (some of them in the range of the petabytes scale).
Keras for model training/generation.
HPC for coordination and node-wide training of hyperparameters.

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