Reducing Uncertainty in Offshore Wind Energy Yield Estimates via a Metocean Reference Site

Andrew Kumler, Julie Lundquist, Caroline Draxl, Anthony Kirincich

Research output: NRELPresentation


The offshore wind industry is burgeoning in the coastal waters of the United States, specifically along the Atlantic. For wind energy to be successful, reliable observations and model simulations are needed for resource assessment and forecasting. While many of these activities have already begun, there is currently an absence of observations at hub-height in these waters, with the closest available hub-height measurements usually taken onshore. Deployment of floating lidars has occurred through various federally funded projects, but only encapsulates time periods of a couple of years at best. Private industry is also beginning to leverage floating lidars, but this data is often proprietary, and not shared with the general public. In this work, we make the case for a metocean reference site for long-term offshore wind energy. Specifically, we quantify the impact of having a metocean reference site compared to other methods of determining hub-height winds and energy production. We use an offshore floating lidar to directly measure the wind resource, and compare these measurements to predictions derived from other widely-available surface meteorological variables. These prediction methods (vertical extrapolation, machine learning, and NWP output) produce a variety of vertical wind speed profiles, of which produce different energy yield estimates for a reference offshore turbine (Figure 1). While some methods perform reasonably well against the lidar, the uncertainty in these energy yield estimates has financial implications, further illustrating the need for long-term measurements in coastal waters.
Original languageAmerican English
Number of pages17
StatePublished - 2023

Publication series

NamePresented at the 103rd American Meteorological Society Annual Meeting, 8-12 January 2023, Denver, Colorado

NREL Publication Number

  • NREL/PR-5000-84925


  • machine learning
  • measurements
  • numerical weather prediction
  • NWP
  • offshore wind
  • random forest
  • uncertainty
  • wind
  • wind energy
  • wind power
  • WRF


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