Ray Use Cases

Wind farm optimization using RL and large-scale simulation

Tuesday, August 23
3:15 PM - 3:45 PM

In collaboration with Microsoft and Vestas, we developed wind farm controllers that boost annual energy production by 1-2%. The controller achieves this by adjusting the yaw of upstream wind turbines in the farm to minimize wake losses at downstream turbines. The training of such deep reinforcement learning-based controllers for wind farms required large-scale and time-consuming computational fluid dynamics simulations. During the training of the controllers we used up to 15,000 CPU cores in parallel, using our platform DeepSim. DeepSim is minds.ai's end-to-end machine learning platform that uses Ray for distributed computing, model serving, and data processing. In this talk, we will discuss the project and how the controllers were efficiently trained in the cloud.

About Jasper

Jasper van Heugten holds a PhD in theoretical physics from Utrecht University. He has experience in developing and scoping deep learning and reinforcement learning solutions for Fortune 100 companies in the renewables, semiconductor, tech, automotive, and pharmaceutical industries. He currently leads minds.ai's product development efforts in the renewable and semiconductor industries.

About Prashant

Prashant Kumar holds a PhD in applied mathematics from the Delft University of Technology. He currently works as a machine learning engineer at minds.ai, developing AI solutions for efficient wind farm design and control. Previously, he co-founded a mobility startup and has extensive experience working with energy companies and AI consulting firms.

Jasper van Heugten

Director of Advanced Technology, minds.ai

Prashant Kumar

Neural Network Engineer, minds.ai
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