Short-term wind-speed forecasting system for wind power applications

Accurate short-term wind speed forecasts for utility-scale large wind farms will be crucial for the U.S. Department of Energy's goal of providing 20% of total electricity from wind by 2030. Communicating the level of uncertainty in these wind speed forecasts will allow the industry to better quantify the level of financial risk inherent with these forecasts. In this study, a computationally efficient and accurate system for short-term (0-60 mins) forecasting of wind speed is developed. This system uses a 27 member ensemble of the Weather Research and Forecasting Single-Column Model (WRF-SCM) to generate a probability density function (pdf) of daytime forecasts at 90m height for a location in Chalmers Township in West/Central Illinois. The WRF-SCM ensemble is initialized by the 20km Rapid Update Cycle (RUC) 00h forecast and perturbed by both perturbations in the initial conditions and physics options. The pdf is calibrated using Bayesian Model Averaging (BMA) where the individual forecasts are weighted according to their performance. This combination of a numerical weather prediction ensemble system and Bayesian statistics allows for accurate and computationally efficient prediction of 1 hour wind speed and the level of uncertainty in the forecasts.