Online Learning of Effective Turbine Wind Speed in Wind Farms: Preprint

Aoife Henry, Michael Sinner, Jennifer King, Lucy Pao

Research output: Contribution to conferencePaper


To develop better wind farm controllers that can meet more complex objectives, methods of modeling the wind turbine wakes at low computational expense are needed. Gaussian process (GP) regression offers a computationally inexpensive framework for learning complex functions from noisy measurements with very few datapoints. In this work, an online learning approach is presented to learn the rotor- averaged wind velocity at downstream wind turbines with GPs, using the available datastream of wind field measurements and wind turbine control set-points. This framework can readily be integrated into model-based controls methods because the model a) is updated online at low computational expense, b) assumes a mathematically favorable Gaussian form, and c) explicitly quantifies the stochastic nature of the wake field so that the trade-off between exploration and exploitation, and the uncertainty in the prediction, can be utilized. We show that a GP-learned model can match true values with errors within 0.5% on average, with as few as 5 training data points.
Original languageAmerican English
Number of pages9
StatePublished - 2024
EventIEEE Conference on Decision and Control - Singapore, Singapore
Duration: 13 Dec 202315 Dec 2023


ConferenceIEEE Conference on Decision and Control
CitySingapore, Singapore

Bibliographical note

See NREL/CP-5000-89048 for paper as published in proceedings

NREL Publication Number

  • NREL/CP-5000-87155


  • Gaussian process
  • machine learning
  • surrogate model
  • wake model


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