The conventional wind farm (WF) power generation modelling method highly relies on wind hindcast produced by record time-series data or numerical weather modelling. However

estimating production at future sites is challenging in the absence of local wind monitoring. To address this

a data-driven WF modelling and model transfer strategy is proposed in this work. It considers the challenge of how to transpose metered data from existing operational WFs to sites that might feature as a prospective site for a new WF. By modelling 14 WFs distributed across Scotland using a machine learning (ML) approach

this study proved it was possible to effectively model metered production at a site using modelled wind speed and direction. In addition

this study also found when the latitude difference between two WFs is less than 0.2 degrees and the distance is less than 50 km

two WFs in non-mountainous areas can share an ML model. The results of the shared ML model remain superior to the results of the given power curve from manufacturers

after adjusting the results by the ratio of the power curve in these two WFs. The WF model transfer strategy investigated in this work offered a novel approach to transposing WF production estimates to new sites and appeared to offer better value than simple power curves

which is of importance at the early planning stage for site selection

although it would likely not fully replace detailed micro-siting modelling which are well established in the industry.