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arXiv Machine Learning By Yuhan Su, Hongyang Dong, Simone Tamaro, Filippo Campagnolo, Carlo L. Bottasso, Xiaowei Zhao

Offline Reinforcement Learning for Wind Farm Control: A Wind Tunnel Study under Dynamic Wind Directions

Read the original on arXiv Machine Learning →

The paper introduces MTD3-BC, a model‑free offline reinforcement learning algorithm that optimizes yaw control for wind farms amid changing wind directions. By learning from a pre‑collected dataset and incorporating an action consistency term, it reduces the need for extensive simulator interactions. Experimental wind‑tunnel tests show that MTD3‑BC improves farm‑level power output by about 10% compared to a greedy baseline and matches a model‑based benchmark, all while cutting training costs dramatically.

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 Machine Learning.

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