Nov 24, 2022 · This work proposes a surrogate model for the representation of wind turbine wakes based on a state-of-the-art graph representation learning ...
The work presented here aims to develop a novel data-driven wake model that is based on machine learning and high-fidelity CFD simulations. In particular, this ...
This work proposes a surrogate model for the representation of wind turbine wakes based on a state-of-the-art graph representation learning method termed a ...
Nov 24, 2022 · This work proposes a surrogate model for the representation of wind turbine wakes based on a state-of-the-art graph representation learning ...
A number of modelling and optimisation approaches have emerged for the intra-farm wake problem including via CFD simulations (Sanderse et al., 2011), ...
End-to-end wind turbine wake modelling with deep graph ... - OUCI
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End-to-end wind turbine wake modelling with deep graph representation learning ... Ti, Wake modeling of wind turbines using machine learning, Appl Energy, № 257
End-to-end wind turbine wake modelling with deep graph representation learning. Published in. Applied Energy, June 2023. DOI, 10.1016/j.apenergy.2023.120928.
Abstract: This work proposes a novel data-driven model capable of providing accurate predictions for the power generation of all wind turbines in wind farms ...
Wind turbine wake modelling is of crucial importance to accurate resource assessment, to layout optimisation, and to the operational control of wind farms.
Learning to optimise wind farms with graph transformers. 1 Apr 2024 ... End-to-end wind turbine wake modelling with deep graph representation learning.