A Novel Analytical Tool to Capture Wind Profile Variability for Wind Energy Assessment: Fast, Simple, and Beyond State-of-the-Art in Complex Terrain

This dataset and accompanying code provide a fully reproducible implementation of a novel analytical model for wind turbine power estimation that incorporates the vertical variation of horizontal wind speed. Unlike IEC-standard methods that rely on a single-point wind measurement at nacelle height, our model applies a Taylor expansion of the inflow along the rotor’s vertical axis, enabling height-resolved integration of shear, veer, and turbulence effects. To illustrate its functionality, we include the Gotthard Pass 2023 campaign dataset (van Schaik et al. 2025a), available at:https://zenodo.org/records/14524723This dataset contains Doppler LiDAR profiles and SCADA power measurements from five Enercon E92 turbines in complex Alpine terrain. Using this data package, users can reproduce the full analysis presented in van Schaik et al. (2025), including idealised wind-profile experiments and validation across 10 distinct wind events. When evaluated against turbine power measurements, the analytical model achieves smoother and more accurate predictions than both OpenFAST simulations and IEC-standard single-point extrapolations, reducing RMSE by 8.8% and bias by 25.4%. For a 2.35 MW Enercon E92 turbine at this site, this corresponds to an annual energy production estimate that is 23.7 MWh closer to observed values. This Zenodo record contains: The 2023 Gotthard Pass LiDAR + relevant SCADA dataset The Python implementation of the Taylor-expansion analytical model Example notebooks showing how to run the model on the provided data Wake-filtering tools for removing wake-affected LiDAR profiles The package is intended for researchers and practitioners working in complex terrain, pre-construction wind assessments, and wind-park expansion planning.