  Heteroscedastic Gaussian process regression, based on the concept of chained
Gaussian processes, is used to build surrogates to predict site-specific loads
on an offshore wind turbine. Stochasticity in the inflow turbulence and
irregular waves results in load responses that are best represented as random
variables rather than deterministic values. Moreover, the effect of these
stochastic sources on the loads depends strongly on the mean environmental
conditions -- for instance, at low mean wind speeds, inflow turbulence produces
much less variability in loads than at high wind speeds. Statistically, this is
known as heteroscedasticity. Deterministic and most stochastic surrogates do
not account for the heteroscedastic noise, giving an incomplete and potentially
misleading picture of the structural response. In this paper, we draw on the
recent advancements in statistical inference to train a heteroscedastic
surrogate model on a noisy database to predict the conditional pdf of the
response. The model is informed via 10-minute load statistics of the
IEA-10MW-RWT subject to both aero- and hydrodynamic loads, simulated with
OpenFAST. Its performance is assessed against the standard Gaussian process
regression. The predicted mean is similar in both models, but the
heteroscedastic surrogate approximates the large-scale variance of the
responses significantly better.
