Wind Farm Wake Steering Data for Diurnal Atmospheric Conditions

Supporting data for the research paper 'Optimal closed-loop wake steering, Part 2: Diurnal cycle atmospheric boundary layer conditions'. The dataset was produced by authors from the Massachusetts Institute of Technology and Stanford University. It likely contains tabular data related to wind farm control simulations under varying atmospheric conditions.

Use Cases

• Training reinforcement learning agents for wind farm wake steering based on simulated atmospheric conditions.
• Benchmarking closed-loop control strategies for wind energy based on diurnal cycle data.
• Analyzing the impact of atmospheric boundary layer dynamics on wind farm power output.

Strengths

• Data is directly linked to a peer-reviewed research paper, providing academic context.
• Associated code is publicly available in a GitHub repository for reproducibility.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Massachusetts Institute of Technology / Stanford University research

Collection Method

Simulation data generated to support the research paper.

Time Range

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Freshness

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Geography

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