Journalartikel

Jahr der Veröffentlichung: 2016

Seiten: 285-297

Zeitschrift: Applied Energy

Bandnummer: 177

ISSN: 0306-2619

eISSN: 1872-9118

Open Access Status: Green

DOI Link: https://doi.org/10.1016/j.apenergy.2016.05.111

Verlag: Elsevier

Abstract: 
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). (C) 2016 Elsevier Ltd. All rights reserved.

Harvard-Zitierstil: Ziel, F., Croonenbroeck, C. and Ambach, D. (2016) Forecasting wind power - Modeling periodic and non-linear effects under conditional heteroscedasticity, Applied Energy, 177, pp. 285-297. https://doi.org/10.1016/j.apenergy.2016.05.111

APA-Zitierstil: Ziel, F., Croonenbroeck, C., & Ambach, D. (2016). Forecasting wind power - Modeling periodic and non-linear effects under conditional heteroscedasticity. Applied Energy. 177, 285-297. https://doi.org/10.1016/j.apenergy.2016.05.111