A Measure–Correlate–Predict Approach for Transferring Wind Speeds from MERRA2 Reanalysis to Wind Turbine Hub Heights

Abstract

Reanalysis datasets, such as MERRA2, are frequently used in wind resource assessments. However, their wind speed data are typically limited to fixed altitudes that differ from wind turbine hub heights, which introduces significant uncertainty in energy yield estimations. To address this challenge, we propose a reproducible Measure–Correlate–Predict (MCP) framework that integrates Random Forest (RF) supervised learning to estimate hub-height wind speeds from MERRA2 data at 50 m. The method includes the fitting of 21 vertical wind profile models using data at 2 m, 10 m, and 50 m, with model selection based on the minimum mean square error. The approach was applied to seven wind-prone locations in the Canary Islands, selected for their strategic relevance in current or planned wind energy development. Results indicate that a three-parameter logarithmic wind profile achieved the best fit in 51.31% of cases, significantly outperforming traditional single-parameter models. The RF-based MCP predictions at different hub heights achieved RMSE metrics below 0.425 m/s across a 10-year period. These findings demonstrate the potential of combining physical modeling with machine learning to enhance wind speed extrapolation from reanalysis data and support informed wind energy planning in data-scarce regions.