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arXiv AI

Noise Steering: Exploiting Wake Steering and Acoustic Directivity for Noise-Constrained Wind Farm Control via Reinforcement Learning

Noise Steering is a reinforcement learning approach that jointly adjusts turbine yaw angles and power setpoints to maximize wind farm power while keeping noise within a 40 dB limit in a protected area. By learning to redirect both wakes and acoustic directivity, the method achieves only a 2–7.7 % power deficit compared to a 17 % loss with standard noise‑reduced operation. The policy remains effective when tested in higher‑fidelity simulations, showing promise for better power‑noise trade‑offs in wind farm control.

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arXiv Machine Learning
Sep 14

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

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.

By Yuhan Su, Hongyang Dong, Simone Tamaro, Filippo Campagnolo, Carlo L. Bottasso, Xiaowei Zhao
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arXiv Machine Learning
Jul 3

Wind-Aware Reinforcement Learning Control of a Small Quadrotor Using Learned Onboard Wind Estimation in Simulated Atmospheric Turbulence

arXiv:2607. 01528v1 Announce Type: new Abstract: Small multirotor aircraft are increasingly tasked with operations in the atmospheric boundary layer, where turbulent winds comparable to the vehicle's airspeed degrade trajectory tracking and can defeat conventional feedback control.

By Abdullah Al Tasim, Wei Sun
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arXiv Statistics ML
Oct 28

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

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.

By Zhen-Fan Wang, Kai Zhang, Xianping Du, Zhaolong Han, Dai Zhou, Onur Bilgen
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Hugging Face Trending Papers
Jun 29

Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning

This paper studies Reinforcement Learning as an online controller for curtailment-aware workload shifting in wind-turbine-integrated high-performance computing (HPC) data centers. We introduce a reproducible fixed-day simulation framework with synthetic wind and price signals and delayed completion feedback, designed to be extensible toward more complex scenarios.

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arXiv Machine Learning
Jul 15

Gradient-free learning of a closed-loop wall controller for turbulent drag reduction

arXiv:2607. 12626v1 Announce Type: cross Abstract: Closed-loop wall control learnt by multi-agent reinforcement learning can lower skin-friction drag in turbulent channels, but these gradient-based policies are trained on small periodic boxes and exhibit reduced performance when carried over to a larger domain.

By Giorgio Maria Cavallazzi, Miguel P\'erez Cuadrado, Alfredo Pinelli
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arXiv Machine Learning
Sep 10

Generalizing HVAC Control With Domain Randomized Reinforcement Learning

The paper introduces NOMAD‑RL, a reinforcement learning controller for HVAC systems that learns to adapt across diverse thermal zones via a universal thermostat interface. It employs an adaptive domain randomization scheme using physics‑informed normalizing flows to generate realistic, multimodal training data, enabling the recurrent policy to handle partial observability. Experiments show NOMAD‑RL outperforms constant‑setpoint PID and non‑randomized RL, and rivals well‑tuned model predictive control, especially in multi‑zone scenarios.

By Pablo Boitel, Kun Zhang
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arXiv AI
Jun 30

Agile Reinforcement Learning through Separable Neural Architecture and Applications

arXiv:2601. 23225v2 Announce Type: replace-cross Abstract: Deep reinforcement learning (RL) is increasingly deployed in resource-constrained environments, yet go-to function approximators - multilayer perceptrons (MLPs) - are often parameter-inefficient due to an imperfect inductive bias for the smooth structure of many value functions.

By Rajib Mostakim, Reza T. Batley, Sourav Saha
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