Forecasting wind power - Modeling periodic and non-linear effects under conditional heteroscedasticity
This work addresses wind power forecasting for energy management, but it is incremental as it builds on existing time-series and GARCH methods with specific adaptations.
The authors tackled the problem of forecasting wind speed and power for a wind park by developing a model combining multivariate seasonal TVARMA with power-TGARCH, incorporating periodicity and conditional heteroscedasticity, and achieved accurate forecasts for up to 48 hours.
In this article we present an approach that enables joint wind speed and wind power forecasts for a wind park. We combine a multivariate seasonal time varying threshold autoregressive moving average (TVARMA) model with a power threshold generalized autoregressive conditional heteroscedastic (power-TGARCH) model. The modeling framework incorporates diurnal and annual periodicity modeling by periodic B-splines, conditional heteroscedasticity and a complex autoregressive structure with non-linear impacts. In contrast to usually time-consuming estimation approaches as likelihood estimation, we apply a high-dimensional shrinkage technique. We utilize an iteratively re-weighted least absolute shrinkage and selection operator (lasso) technique. It allows for conditional heteroscedasticity, provides fast computing times and guarantees a parsimonious and regularized specification, even though the parameter space may be vast. We are able to show that our approach provides accurate forecasts of wind power at a turbine-specific level for forecasting horizons of up to 48 h (short- to medium-term forecasts).