Predicting Linearised Wind Resource Grids using Convolutional Neural Networks in Style Transfer and Encoder-Decoder Based Architectures

Research output: Contribution to conference › Conference Abstract › peer-review

Original language	English
Publication status	Published - 2021
Event	Wind Energy Science Conference 2021 - Hannover, Germany Duration: 25 May 2021 → 28 May 2021 https://www.wesc2021.org/en/

Conference	Wind Energy Science Conference 2021
Abbreviated title	WESC
Country/Territory	Germany
City	Hannover
Period	25/05/21 → 28/05/21
Internet address	https://www.wesc2021.org/en/

Handle.net

Machine learning for wind flow modelling: using grid-based neural networks to capture wind flow changes over terrain
Sheehan, H. M. (Author), Poole, D. (Supervisor) & Harper, P. (Supervisor), 22 Mar 2022
Student thesis: Master's Thesis › Master of Science by Research (MScR)

File

@conference{40ae4960c20446dd9a1a98a3cc07dc22,

title = "Predicting Linearised Wind Resource Grids using Convolutional Neural Networks in Style Transfer and Encoder-Decoder Based Architectures",

author = "Sheehan, \{Helen M\} and Elizabeth Traiger and Poole, \{Daniel J\} and Lars Landberg",

year = "2021",

language = "English",

note = "Wind Energy Science Conference 2021, WESC ; Conference date: 25-05-2021 Through 28-05-2021",

url = "https://www.wesc2021.org/en/",

}