کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6682700 | 501851 | 2016 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Forecasting wind power - Modeling periodic and non-linear effects under conditional heteroscedasticity
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
مهندسی انرژی و فناوری های برق
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چکیده انگلیسی
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 48Â h (short- to medium-term forecasts).
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Energy - Volume 177, 1 September 2016, Pages 285-297
Journal: Applied Energy - Volume 177, 1 September 2016, Pages 285-297
نویسندگان
Florian Ziel, Carsten Croonenbroeck, Daniel Ambach,