What challenges did Vestas face before AI implementation?

Suboptimal energy capture due to wake effects between turbines in wind farms Lengthy development cycles for optimizing turbine efficiency improvements Growing urgency to meet aggressive renewable energy targets for climate change mitigation Difficulty translating academic insights on wake steering into rapid, real-world deployments

What was the impact of the AI solution?

Proof of concept developed within a few months, dramatically shortening innovation cycle Demonstrated substantial potential for increasing overall wind farm energy output Enabled faster adoption of advanced AI solutions in operational wind farms Contributed to Vestas earning the 'Best Use of High-Performance Computing in Energy' award in 2023

Source

MicrosoftExpanded

Vestas optimizes wind energy efficiency with Azure AI

Use case typeWind optimizationUpdated Jun 13, 2026

Vestas, a leading wind turbine manufacturer, partnered with Microsoft and minds.ai to boost wind energy efficiency using advanced AI and high-performance computing. By deploying Azure Machine Learning and Azure's computational power, Vestas optimized wake steering, a technique enhancing the efficiency of downstream turbines. Within just a few months, a proof of concept leveraging reinforcement learning was built using minds.ai's DeepSim platform. This project highlighted substantial potential for untapped clean energy capture and earned Vestas the 'Best Use of High-Performance Computing in Energy' award in 2023.

Organization: Vestas
Industry: Energy & Utilities
Location: Denmark
Published: March 2022

Reported outcomes

Strategic outcomes

New product / capabilityOptimized wake steering strategiesSpeed & agilityShortened innovation cycleNew product / capabilityBuilt reinforcement learning proof of conceptCompetitive differentiationEarned industry recognition award

Primary read

Use case focus

Showing 3 of 3

1AI-driven wake steering optimization for wind farms
2Reinforcement learning for predictive turbine control
3High-performance computing for large-scale wind energy simulations

Suboptimal energy capture due to wake effects between turbines in wind farms
Lengthy development cycles for optimizing turbine efficiency improvements
Growing urgency to meet aggressive renewable energy targets for climate change mitigation
Difficulty translating academic insights on wake steering into rapid, real-world deployments

Implemented reinforcement learning models via minds.ai's DeepSim platform on Azure
Leveraged Azure Machine Learning and Azure HPC resources for rapid simulation and training
Optimized wake steering strategies to maximize downstream turbine efficiency

Technologies

Azure AI Azure ML

Proof of concept developed within a few months, dramatically shortening innovation cycle
Demonstrated substantial potential for increasing overall wind farm energy output
Enabled faster adoption of advanced AI solutions in operational wind farms
Contributed to Vestas earning the 'Best Use of High-Performance Computing in Energy' award in 2023

Implementation partners1

minds.ai

Sources & evidence1

ExpandedExpanded

The same organization appears in newer AI deployment evidence.