Site analysis to determine the loads experienced by wind turbines based on site-specific environmental conditions is typically done using either coupled aero-servo-elastic simulations for onshore wind turbines or coupled aero-servo-hydroelastic simulations in the case of offshore wind turbines. These simulations become computationally expensive when multiple load cases are needed to be taken into account, together with the numerous possible combinations of turbulence inflow patterns that result in the same mean inflow conditions. Probabilistic surrogate models offer a cheap alternative to these expensive simulations for predicting load statistics. This thesis explores a type of neural network called Conditional Generative Adversarial Networks (CGANs) as a potential candidate for such a surrogate model. Originally developed for image generation, CGANs have seen success in other applications. However, most applications of GANs to date are high-dimensional, with relatively low research focused on low-dimensional problems such as wind turbine load statistics. Multiple experiments are conducted using various multimodal and heteroscedastic datasets to assess its ability to model such characteristics
accurately. The conditional log-likelihood andWasserstein-1 distances were used as metrics. The results show that CGANs can indeed model such low-dimensional datasets. Finally, the CGANs are trained on data from simulations of onshore and offshore wind turbines in OpenFAST and compared with predictions from Mixture Density Networks (MDNs). The results from CGANs are comparable to MDNs, showing its potential as another alternative surrogate method, although more research needs to be performed.