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Nearshore wind estimation from buoy wave spectra via physics-guided machine learning: A framework for offshore wind applications

Author

Listed:
  • Chen, Jiaxin
  • Hann, Martyn
  • Rawlinson-Smith, Robert
  • Raji, Munira
  • Greaves, Deborah

Abstract

Reliable real-time wind estimation in nearshore zones is critical for offshore wind energy development, especially in regions lacking direct meteorological observations. This study presents a physics-guided machine learning framework for estimating 10-m surface wind speed and direction from wave buoy spectra. The model incorporates frequency- and wavenumber-based energy parameters, directional Fourier coefficients, directional fetch, and normalised wave number–depth indicators to account for local wind–wave dynamics.

Suggested Citation

  • Chen, Jiaxin & Hann, Martyn & Rawlinson-Smith, Robert & Raji, Munira & Greaves, Deborah, 2026. "Nearshore wind estimation from buoy wave spectra via physics-guided machine learning: A framework for offshore wind applications," Renewable Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:renene:v:259:y:2026:i:c:s0960148125026643
    DOI: 10.1016/j.renene.2025.125000
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    References listed on IDEAS

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