  In the present paper we present a surrogate model, which can be used to
estimate extreme tower loads on a wind turbine from a number of signals and a
suitable simulation tool. Due to the requirements of the International
Electrotechnical Commission (IEC) Standard 61400-1, assessing extreme tower
loads on wind turbines constitutes a key component of the design phase. The
proposed model imputes tower loads by matching observed signals with simulated
quantities using proximities induced by random forests. In this way the
algorithm's adaptability to high-dimensional and sparse settings is exploited
without using regression-based surrogate loads (which may display misleading
probabilistic characteristics). Finally, the model is applied to estimate tower
loads on an operating wind turbine from data on its operational statistics.
