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Transformation of reanalysis data for improved long-term estimation of wind speed and direction at a target site

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  • Carta, José A.
  • Cabrera, Pedro

Abstract

This paper proposes the use of measure-correlate-predict (MCP) methods based on supervised machine learning (ML) techniques to transform reanalysis data from ERA5 and MERRA2, aiming to improve the long-term estimation of wind speed and direction at locations with limited on-site measurements. The study analyzes models that directly estimate the target variables—wind speed and direction—as well as two-stage models that first estimate the Cartesian components of wind velocity and subsequently transform them into polar coordinates.

Suggested Citation

  • Carta, José A. & Cabrera, Pedro, 2026. "Transformation of reanalysis data for improved long-term estimation of wind speed and direction at a target site," Renewable Energy, Elsevier, vol. 261(C).
    • Handle: RePEc:eee:renene:v:261:y:2026:i:c:s0960148126001059
    DOI: 10.1016/j.renene.2026.125280
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