Conditional weighted combination of wind power forecasts

Abstract

Abstract The classical regression model for combined forecasting was reformulated in order to impose restrictions on the combination weights. This restricted linear combination model was then extended to the case where the weights were allowed to be a non‐parametric function of some meteorological variables, yielding the so‐called conditional weighted combination method. The weight functions are estimated with local regression techniques. The conditional weighted combination method was applied to a test case of a wind farm over a period of 10 months. Various combinations of two forecasts out of the three available ones were considered. Analysis of the data suggested that meteorological forecasts of air density and turbulent kinetic energy may be considered as relevant external variables in the combination scheme. A performance comparison showed that the conditional weighted combination method introduced in this paper outperformed the least‐squares combination method for almost all prediction horizons, especially for larger ones. This indicates that further developments based on conditional combination methods, including adaptivity of the weight functions estimation, may significantly enhance forecast accuracy and dampen the risk of large prediction errors. Copyright © 2010 John Wiley & Sons, Ltd.