Preprints
https://doi.org/10.5194/wes-2021-33
https://doi.org/10.5194/wes-2021-33

  11 May 2021

11 May 2021

Review status: this preprint is currently under review for the journal WES.

Assessing Boundary Condition and Parametric Uncertainty in Numerical-Weather-Prediction-Modeled, Long-Term Offshore Wind Speed Through Machine Learning and Analog Ensemble

Nicola Bodini1, Weiming Hu2, Mike Optis1, Guido Cervone2,3, and Stefano Alessandrini3 Nicola Bodini et al.
  • 1National Renewable Energy Laboratory, Golden, Colorado, USA
  • 2Department of Geography, Pennsylvania State University, University Park, PA, 16802, USA
  • 3National Center for Atmospheric Research, Boulder, CO, USA

Abstract. To accurately plan and manage wind power plants, not only does the time-varying wind resource at the site of interest need to be assessed, but also the uncertainty connected to this estimate. Numerical weather prediction (NWP) models at the mesoscale represent a valuable way to characterize the wind resource offshore, given the challenges connected with measuring hub height wind speed. The boundary condition and parametric uncertainty associated with modeled wind speed is often estimated by running a model ensemble. However, creating an NWP ensemble of long-term wind resource data over a large region represents a computational challenge. Here, we propose two approaches to temporally extrapolate wind speed boundary condition and parametric uncertainty using a more convenient setup where a mesoscale ensemble is run over a short-term period (1 year), and only a single model covers the desired long-term period (20 year). We quantify hub-height wind speed boundary condition and parametric uncertainty from the short-term model ensemble as its normalized across-ensemble standard deviation. Then, we develop and apply a gradient-boosting model and an analog ensemble approach to temporally extrapolate such uncertainty to the full 20-year period, where only a single model run is available. As a test case, we consider offshore wind resource characterization in the California Outer Continental Shelf. Both the proposed approaches provide accurate estimates of the long-term wind speed boundary condition and parametric uncertainty across the region (R2 > 0.75), with the gradient-boosting model slightly outperforming the analog ensemble in terms of bias and centered root-mean-square error. At the three offshore wind energy lease areas in the region, we find a long-term median hourly uncertainty between 10 % and 14 % of the mean hub-height wind speed values. Finally, we assess the physical variability of the uncertainty estimates. In general, we find that the wind speed uncertainty increases closer to land. Also, neutral conditions have smaller uncertainty than the stable and unstable cases, and the modeled wind speed in winter has less boundary condition and parametric sensitivity than summer.

Nicola Bodini et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2021-33', Anonymous Referee #1, 20 May 2021
  • RC2: 'Comment on wes-2021-33', Anonymous Referee #2, 08 Jul 2021

Nicola Bodini et al.

Data sets

WRF-based wind speed dataset for the California outer Continental Shelf Mike Optis et al. https://developer.nrel.gov/docs/wind/wind-toolkit/offshore-ca-download/

Model code and software

Machine learning code for offshore UQ Nicola Bodini https://github.com/nbodini/ML_UQ_offshore

AnEn code Weiming Hu et al. https://weiming-hu.github.io/AnalogsEnsemble

Nicola Bodini et al.

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Short summary
We develop two machine-learning-based approaches to temporally extrapolate uncertainty in hub-height wind speed modeled by a numerical weather prediction model. We test our approaches in the California Outer Continental Shelf, where a significant offshore wind energy development is currently being planned, and we find that both provide accurate results.