Very-Short-Term Probabilistic Wind Power Forecasts by Sparse Vector Autoregression

Abstract

A spatio-temporal method for producing very-short-term parametric probabilistic wind power forecasts at a large number of locations is presented. Smart grids containing tens, or hundreds, of wind generators require skilled very-short-term forecasts to operate effectively and spatial information is highly desirable. In addition, probabilistic forecasts are widely regarded as necessary for optimal power system management as they quantify the uncertainty associated with point forecasts. Here, we work within a parametric framework based on the logit-normal distribution and forecast its parameters. The location parameter for multiple wind farms is modeled as a vector-valued spatio-temporal process and the scale parameter is tracked by modified exponential smoothing. A state-of-the-art technique for fitting sparse vector autoregressive models is employed to model the location parameter and demonstrates numerical advantages over conventional vector autoregressive models. The proposed method is tested on a dataset of 5 min mean wind power generation at 22 wind farms in Australia. Five-min ahead forecasts are produced and evaluated in terms of point, and probabilistic forecast skill scores and calibration. Conventional autoregressive and vector autoregressive models serve as benchmarks.