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In recent years, wake steering has been established as a promising method toincrease the energy yield of a wind farm. Current practice in estimating thebenefit of wake steering on the annual energy production (AEP) consists ofevaluating the wind farm with simplified surrogate models, casting a largeuncertainty on the estimated benefit. This paper presents a framework fordetermining the benefit of wake steering on the AEP, incorporating simulationresults from a surrogate model and large eddy simulations in order to reducethe uncertainty. Furthermore, a time-varying wind direction is considered for abetter representation of the ambient conditions at the real wind farm site.Gaussian process regression is used to combine the two data sets into a singleimproved model of the energy gain. This model estimates a 0.60% gain in AEP forthe considered wind farm, which is a 76% increase compared to the estimate ofthe surrogate model.

Predicting the benefit of wake steering on the annual energy production of a wind farm using large eddy simulations and Gaussian process regression