Overview of Data-Driven Models for Wind Turbine Wake Flows

Abstract With the rapid advancement of machine learning technology and its growing adoption in research and engineering applications, an increasing number of studies have embraced data-driven approaches for modeling wind turbine wakes. These models leverage the ability to capture complex, high-dimensional characteristics of wind turbine wakes while offering significantly greater efficiency in the prediction process than physics-driven models. As a result, data-driven wind turbine wake models are regarded as powerful and effective tools for predicting wake behavior and turbine power output. This paper aims to provide a concise yet comprehensive review of existing studies on wind turbine wake modeling that employ data-driven approaches. It begins by defining and classifying machine learning methods to facilitate a clearer understanding of the reviewed literature. Subsequently, the related studies are categorized into four key areas: wind turbine power prediction, data-driven analytic wake models, wake field reconstruction, and the incorporation of explicit physical constraints. The accuracy of data-driven models is influenced by two primary factors: the quality of the training data and the performance of the model itself. Accordingly, both data accuracy and model structure are discussed in detail within the review.