Retrospective de-trending of wind site turbulence using machine learning

Tough, F. and Hart, E. (2022) Retrospective de-trending of wind site turbulence using machine learning. Wind Energy, 25(7), pp. 1173-1187. (doi: 10.1002/we.2720)

Abstract

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Tough, Mr Fraser
• Authors: Tough, F., and Hart, E.
• College/School: College of Science and Engineering > School of Mathematics and Statistics
• Journal Name: Wind Energy
• Publisher: Wiley
• ISSN: 1095-4244
• ISSN (Online): 1099-1824
• Published Online: 17 February 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Wind Energy 25(7): 1173-1187
• Publisher Policy: Reproduced under a Creative Commons licence