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Predicting wind farm operations with machine learning and the P2D‐RANS model: A case study for an AWAKEN site
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Wind-Turbine and Wind-Farm Flows: A Review
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Simulations of an Offshore Wind Farm Using Large-Eddy Simulation and a Torque-Controlled Actuator Disc Model
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Implementation of novel hybrid approaches for power curve modeling of wind turbines
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The role of renewable energy in the global energy transformation
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Experimental characterization of wind turbine wakes: Wind tunnel tests and wind LiDAR measurements
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Optimization of wind farm turbines layout using an evolutive algorithm
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The wind energy (r)evolution: A short review of a long history
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Wake effect modeling: A review of wind farm layout optimization using Jensen׳s model
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The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production
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SciPy 1.0: fundamental algorithms for scientific computing in Python
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Improved clustering and deep learning based short-term wind energy forecasting in large-scale wind farms
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Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements
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Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows
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Data-driven fluid mechanics of wind farms: A review
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Inflow modeling for wind farm flows in RANS
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A numerical investigation of a wind turbine wake in non-neutral atmospheric conditions
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How generalizable is a machine-learning approach for modeling hub-height turbulence intensity?
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Accounting for the effect of turbulence on wind turbine power curves
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Data-driven RANS for simulations of large wind farms
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LES and RANS simulation of onshore Bessaker wind farm: analysing terrain and wake effects on wind farm performance
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Modeling of wind turbine power curve based on Gaussian process
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Recurrent Neural Network Architecture Search for Geophysical Emulation
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Flow Structure and Turbulence in Wind Farms
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Large-Eddy Simulation of Wind-Plant Aerodynamics: Preprint
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Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique
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Wind Farm Modeling with Interpretable Physics-Informed Machine Learning
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Materials for Wind Turbine Blades: An Overview
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Scikit-learn: Machine Learning in Python
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Maximizing Insights from Wind Turbine Data Through Data-Driven Approaches
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Evaluation of forecasted wind speed at turbine hub height and wind ramps by five NWP models with observations from 262 wind farms over China
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Short‐Term Offshore Wind Power Prediction Based on Significant Weather Process Classification and Multitask Learning Considering Neighboring Powers
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Augmenting insights from wind turbine data through data-driven approaches
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Machine learning and hybrid intelligence for wind energy optimization: A comprehensive state-of-the-art review
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A hybrid-calibrated improved dynamic wake meandering model for wind farm wake simulation and active control analysis
inferred← citeshttps://doi.org/10.1063/5.0211259openalex-snapshot
Quantification and assessment of the atmospheric boundary layer height measured during the AWAKEN experiment by a scanning LiDAR
inferred← citeshttps://doi.org/10.1080/15567036.2024.2392890openalex-snapshot
Machine learning approaches in predicting the wind power output and turbine rotational speed in a wind farm
inferred← citeshttps://doi.org/10.1108/k-01-2024-0022openalex-snapshot
A study on site selection of wind power plant based on prospect theory and VIKOR: a case study in China
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Investigating the Impact of System Parameters on Flow-Induced Vibration Hard Galloping Based on Deep Neural Network
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AI-Augmented FEA for Offshore Wind Turbine Equipment: A State-of-the-Art Methodology for Accelerated Fabrication Review
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