Park, JinkyooJinkyooParkSmarsly, KayKaySmarslyLaw, Kincho H.Kincho H.LawHartmann, DietrichDietrichHartmann2022-12-012022-12-012013-06Computing in Civil Engineering - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering: 113-120 (2013)http://hdl.handle.net/11420/14231Site-specific wind field characteristics have a significant impact on the structural response and the lifespan of wind turbines. This paper presents a machine learning approach towards analyzing and predicting the response of wind turbine structures to varying wind field characteristics. Machine learning algorithms are applied (i) to better understand changes of wind field characteristics due to atmospheric conditions, and (ii) to gain new insights into the wind turbine loads being affected by fluctuating wind. Using Gaussian Mixture Models, the variations in wind field characteristics are investigated by comparing the joint probability distribution functions of several wind field features, which are constructed from long-term monitoring data taken from a 500-kW wind turbine in Germany. Furthermore, based on Gaussian Discriminative Analysis, representative daytime and nocturnal wind turbine loads are predicted, compared, and analyzed. © 2013 American Society of Civil Engineers.enTechnikConference Paper10.1061/9780784413029.015Other