1 A machine learning approach for analyzing and predicting

15 16 Site-and time-specific wind field characteristics have a significant impact on the 17 structural response and the lifespan of wind turbines. This paper presents a machine 18 learning approach towards analyzing and predicting the response of a wind turbine 19 structure to diurnal and nocturnal wind fields. Machine learning algorithms are applied (i) 20 to better understand the changes of wind field characteristics due to atmospheric 21 conditions, and (ii) to gain insights into the wind turbine loads being affected by the wind 22 field. Using Gaussian Mixture Models, the variations in wind field characteristics are 23 2 investigated by comparing the joint probability density functions of selected wind field 24 features. The wind field features are constructed from long-term monitoring data taken 25 from a 500 kW wind turbine in Germany that is used as a reference system. Furthermore, 26 employing Gaussian Discriminant Analysis, representative daytime and nocturnal wind 27 turbine loads are compared and analyzed.