Retrospective de-trending of wind site turbulence using machine learning

Fraser Tough, Edward Hart

Research output: Contribution to journalArticlepeer-review

3 Downloads (Pure)


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.

Original languageEnglish
Number of pages15
JournalWind Energy
Early online date17 Feb 2022
Publication statusE-pub ahead of print - 17 Feb 2022


  • turbulence
  • de-trending
  • resource assessment
  • site conditions
  • machine learning
  • wind energy

Cite this