Reliable power forecasts are essential for the grid integration of offshore wind. This work presents a physics-based forecasting framework that couples mesoscale numerical weather prediction with large-eddy simulation (LES) and an actuator-disk turbine representation to predict farm-scale flows and power under realistic atmospheric conditions. Mean meteorological profiles from the Weather Research and Forecasting model drive a concurrent–precursor LES generating turbulent inflow consistent with the evolving boundary layer, while a main LES resolves turbulence and wake formation within the wind farm. The LES configuration and turbine-forcing implementation are validated against canonical single- and multi-turbine benchmarks, showing close agreement in wake deficits and recovery trends. The framework is then demonstrated for the South Fork Wind project (12 turbines, ∼132 MW) using a set of time-varying cases over a 24 h period. Simulations reproduce hub-height wind variability, row-to-row power differences associated with wake interactions, and turbine-level power fluctuations (order 1 MW) that converge with appropriate averaging windows. The results illustrate how an LES-augmented hierarchical modeling system can complement conventional forecasting by providing physically interpretable flow fields and power estimates at operational scales.
Zaman et al. (2025) studied this question.