Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble

Nicola Bodini,Weiming Hu,Guido Cervone,Mike Optis,Stefano Alessandrini

doi:10.5194/wes-6-1363-2021

Abstract

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 in which 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, for which only a single model run is available. As a test case, we consider offshore wind resource characterization in the California Outer Continental Shelf. Both of 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 in 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.

Highlights

Offshore wind energy keeps increasing its market penetration as an inexpensive and clean source of energy
Numerical weather prediction (NWP) models at the mesoscale are often used to obtain an in space and time continuous mapping of the available offshore wind resource at the heights relevant for commercial wind power plant deployment (e.g., Mattar and Borvarán, 2016; Salvação and Soares, 2018), with some studies (Papanastasiou et al, 2010; Steele et al, 2013; Arrillaga et al, 2016) focusing on the validation of modeled coastal wind effects, such as sea breezes, which have a significant impact on offshore wind energy production (Archer et al, 2014)
As offshore wind energy becomes a widespread source of clean energy worldwide, the importance of having an accurate, long-term characterization of the offshore wind resource is crucial, in terms of its mean value and of the uncertainty associated with this estimate

Summary

Introduction

Offshore wind energy keeps increasing its market penetration as an inexpensive and clean source of energy. The often prohibitive costs connected to both these measurement solutions limit their availability to a handful of locations despite recent efforts in leveraging their punctual hub-height measurements for wind speed vertical extrapolation over a larger region (Bodini and Optis, 2020; Optis et al, 2021a) Given these constraints, numerical weather prediction (NWP) models at the mesoscale are often used to obtain an in space and time continuous mapping of the available offshore wind resource at the heights relevant for commercial wind power plant deployment (e.g., Mattar and Borvarán, 2016; Salvação and Soares, 2018), with some studies (Papanastasiou et al, 2010; Steele et al, 2013; Arrillaga et al, 2016) focusing on the validation of modeled coastal wind effects, such as sea breezes, which have a significant impact on offshore wind energy production (Archer et al, 2014). We consider wind speed characterization in the California OCS, and we propose and compare two innovative techniques for modeled wind speed long-term boundary condition and parametric uncertainty quantification.

Numerical simulation setup

Machine-learning approach

Analog ensemble approach

Validation of the proposed approaches

Conclusions