Open AccessRetrospective de‐trending of wind site turbulence using machine learning
Author(s) -
Tough Fraser,
Hart Edward
Publication year - 2022
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.2720
Subject(s) - artificial neural network , range (aeronautics) , random forest , turbulence , wind speed , regression , linear regression , machine learning , computer science , artificial intelligence , meteorology , statistics , mathematics , physics , engineering , aerospace engineering
This paper considers the removal of low‐frequency trend contributions from turbulence intensity values at sites for which only 10‐min statistics in wind speed are available. It is proposed the problem be reformulated as a direct regression task, solvable using machine learning techniques in conjunction with training data formed from measurements at sites for which underlying (non‐averaged) wind data are available. Once trained, the machine learning models can de‐trend sites for which only 10‐min statistics have been retained. A range of machine learning techniques are tested, for cases of linear and filtered approaches to de‐trending, using data from 14 sites. Results indicate this approach allows for excellent approximation of de‐trended turbulence intensity distributions at unobserved sites, providing significant improvements over the existing recommended method. The best results were obtained using Neural Network, Random Forest and Boosted Tree models.