Data analytics methods for wind energy applications

Yu Ding, Jiong Tang, Jianhua Z. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


In the wind industry, it is important to assess a turbine systems response under different wind profiles. For instance, a wind-to-power relationship is crucial for wind power forecast, and a wind-to-stress relationship is important for selecting critical design parameters meeting the reliability requirement. Given the complexity involved in a turbine system, it is impossible to write a neat, analytical expression to underlie the abovementioned relationships. Almost invariably does the wind industry resort to data driven methods for a solution, namely that wind data and the corresponding turbine response data (bending moments or power outputs) are used together to fit empirically the functional relationship of interest. This paper presents a couple of nonparametric data analytic methods relevant to wind energy applications with real life example for demonstration.
Original languageEnglish (US)
Title of host publicationVolume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy
PublisherAmerican Society of Mechanical Engineers
ISBN (Print)9780791856802
StatePublished - Aug 12 2015
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-24
Acknowledged KAUST grant number(s): KUS-CI-016-04
Acknowledgements: Ding and Tang were partially supported by the grants from NSF (CMMI-1300560 and CMMI-1300236). Ding and Huang were partially supported by the grant from King Abdullah University of Science and Technology (KUS-CI-016-04).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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