Active Subspaces for Wind Plant Surrogate Modeling

Ryan King, Julian Quick, Christiane Adcock, Katherine Dykes

Research output: Contribution to conferencePaperpeer-review

3 Scopus Citations


Understanding the uncertainty in wind plant performance is crucial to their cost-effective design and operation. However, conventional approaches to uncertainty quantification (UQ), such as Monte Carlo techniques or surrogate modeling, are often computationally intractable for utility-scale wind plants because of poor congergence rates or the curse of dimensionality. In this paper we demonstrate that wind plant power uncertainty can be well represented with a low-dimensional active subspace, thereby achieving a significant reduction in the dimension of the surrogate modeling problem. We apply the active sub-spaces technique to UQ of plant power output with respect to uncertainty in turbine axial induction factors, and find a single active subspace direction dominates the sensitivity in power output. When this single active subspace direction is used to construct a quadratic surrogate model, the number of model unknowns can be reduced by up to 3 orders of magnitude without compromising performance on unseen test data. We conclude that the dimension reduction achieved with active subspaces makes surrogate-based UQ approaches tractable for utility-scale wind plants.

Original languageAmerican English
Number of pages11
StatePublished - 2018
EventWind Energy Symposium, 2018 - Kissimmee, United States
Duration: 8 Jan 201812 Jan 2018


ConferenceWind Energy Symposium, 2018
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2018 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

NREL Publication Number

  • NREL/CP-2C00-71342


  • fuel additives
  • Monte Carlo methods
  • uncertainty analysis
  • wind power


Dive into the research topics of 'Active Subspaces for Wind Plant Surrogate Modeling'. Together they form a unique fingerprint.

Cite this