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arXiv Statistics ML By Zhen-Fan Wang, Kai Zhang, Xianping Du, Zhaolong Han, Dai Zhou, Onur Bilgen

A Graph-informed Kriging model for sample-efficient Bayesian optimization of wind farm layouts

Read the original on arXiv Statistics ML →

The paper introduces a Graph-informed Kriging framework that embeds aerodynamic interaction data into the surrogate mean function for wind farm layout optimization. By representing the farm as a directed aerodynamic graph and assembling pairwise turbine interactions into a farm-scale mean, the method captures dominant variation in annual energy production, allowing the Gaussian process to focus on residuals. Numerical experiments demonstrate that this approach improves AEP prediction accuracy and sample efficiency over conventional Kriging, achieving competitive solutions on the IEA Wind Task 37 benchmark with only about 280 evaluations and outperforming other methods in both 16- and 48-turbine cases.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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