Abstract
Abstract. Accurate characterization of the offshore wind resource has been hindered by a sparsity of wind speed observations that span offshore wind turbine rotor-swept heights. Although public availability of floating lidar data is increasing, most offshore wind speed observations continue to come from buoy-based and satellite-based near-surface measurements. The aim of this study is to develop and validate novel vertical extrapolation methods that can accurately estimate wind speed time series across rotor-swept heights using these near-surface measurements. We contrast the conventional logarithmic profile against three novel approaches: a logarithmic profile with a long-term stability correction, a single-column model, and a machine-learning model. These models are developed and validated using 1 year of observations from two floating lidars deployed in US Atlantic offshore wind energy areas. We find that the machine-learning model significantly outperforms all other models across all stability regimes, seasons, and times of day. Machine-learning model performance is considerably improved by including the air–sea temperature difference, which provides some accounting for offshore atmospheric stability. Finally, we find no degradation in machine-learning model performance when tested 83 km from its training location, suggesting promising future applications in extrapolating 10 m wind speeds from spatially resolved satellite-based wind atlases.
Highlights
The accurate characterization of the offshore wind resource is crucial for a range of analyses needed to support the growing offshore wind industry
We evaluate the performance of the learning algorithm based on the root-meansquare error (RMSE) between the measured and predicted wind speed at extrapolation height: the set of hyperparameters that leads to the lowest RMSE is selected and used to assess the final performance of the learning algorithm
We developed novel methods for the vertical extrapolation of near-surface offshore wind speeds
Summary
The accurate characterization of the offshore wind resource is crucial for a range of analyses needed to support the growing offshore wind industry. Accurate time series estimates of wind speed across the rotor-swept heights of an offshore wind turbine are used for estimates of turbine and wind plant power production, which feed into various technical and economic analyses, ranging from grid integration (Mahoney et al, 2012), life-cycle cost analyses (Jong et al, 2017), and capacity expansion studies (Hasager et al, 2015). Buoy-mounted floating lidar, are emerging as a game-changing technology, especially in the United States, providing accurate wind speed and direction measurements up to approximately 250 m (Carbon Trust, 2018); these units are expensive, mostly owned by wind plant developers, and their data are kept highly proprietary. In the United States, for example, as of December 2020, there are only six publicly available data sources for floating lidar in US offshore waters (Table 1)
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