Short-Term Ensemble NWP Wind Speed Forecasts using Mean-Variance Portfolio Optimization and Neural Networks

The effective short-term forecasting of wind speeds for wind farm applications requires the tailored application of a collection of various forecasting tools. Physical forecasting methods generally form the basis of these forecasting tools, coupled with regression and statistical methods. In this work, the Weather Research and Forecasting (WRF) model is utilized to generate an ensemble spread for a targeted site. The optimal deterministic forecast is subsequently determined using two techniques: a novel application of the mean-variance portfolio theorem; as well as a two-layered feed forward neural network. The mean-variance portfolio optimization technique is assessed for its suitability for weighted ensemble selection by comparing its optimized deterministic forecast accuracy with a neural network approach.