Zusammenfassung
Mit der Energiewende in Deutschland steigt die Bedeutung der Vorhersage von Windenergie sowie Windgeschwindigkeit. Sowohl kurz- als auch mittelfristige genaue Vorhersagen von Windgeschwindigkeit und Windenergie spielen eine entscheidende Rolle in verschiedenen Wirtschaftsbereichen. Die Weiterentwicklung und Anwendung neuer Windvorhersagemodelle hilft dabei, den Nutzen aus Windenergie zu erhöhen. Der vorliegende Beitrag umfasst die Anwendung eines periodisch-saisonalen vektorautoregressiven Prognosemodells (VAR), um die mittlere Windgeschwindigkeit vorherzusagen. Darüber hinaus beinhaltet das betrachtete Modell autoregressive bedingte Heteroskedastizität basierend auf Schwellenwerten (TARCH). Die eingeführten Modelle werden mit Hilfe der Methode der kleinsten absoluten Minderung und Selektion (LASSO) mit iterativer Neugewichtung geschätzt. Dieser Ansatz wird wiederum mit der Methode der kleinsten Quadrate (OLS), der klassischen LASSO-Methode sowie weiteren gängigen Bezugsmethoden verglichen. Weiterhin sind die entsprechenden Vorhersagen der Windenergie zu ermitteln. Die Güte der betrachteten Modelle wird im Rahmen der Schätzung sowie anhand der Genauigkeit bei der Vorhersage beurteilt und diskutiert. Es werden Prognosen für die nachfolgenden 48 h bestimmt. Abschließ end werden die Windgeschwindigkeitsvorhersagen mit Hilfe der Nennleistungskennlinie in Windenergie transformiert. Hierbei wird ebenfalls die Genauigkeit der Vorhersage beurteilt.
Abstract
The importance of wind power as well as wind speed predictions increases with energy transition in Germany. Accurate short and medium term wind speed and wind energy predictions are essential in different fields of economy. Implementation and application of new wind forecasting models helps to increase the benefit of wind power. This paper deals with applications of the periodic seasonal vector autoregressive prediction model (VAR). Moreover, the threshold autoregressive conditional heteroscedasticity (TARCH) is considered. The introduced model is estimated using iteratively reweighted least absolute shrinkage and selection (LASSO). This method is compared to ordinary least squares (OLS) estimation, a pure LASSO approach as well as several benchmark models. In addition, wind energy predictions are computed. Moreover, both the in-sample performance and their out-of-sample accuracy are discussed. The findings are summarized in an overview of different time series wind speed prediction models and their accuracy. We provide the forecasting performance up to 48 h. Finally, we cover the problem of transforming wind speed to wind energy.
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Notes
Das Azimut bzw. die Himmelsrichtung bezeichnet den Winkel im Koordinatensystem des Horizonts und in dem hier betrachteten Fall den geographischen Norden. Deshalb entspricht null Grad der Windrichtung Norden und folglich steigt der Winkel im Uhrzeigersinn.
Auf die Darstellung der Ergebnisse für die Windkraftanlage 2 bis 4 wird verzichtet, da die restlichen Abbildungen dieselben Ergebnisse aufzeigen.
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Ambach, D., Garthoff, R. Vorhersagen der Windgeschwindigkeit und Windenergie in Deutschland. AStA Wirtsch Sozialstat Arch 10, 15–36 (2016). https://doi.org/10.1007/s11943-016-0177-1
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DOI: https://doi.org/10.1007/s11943-016-0177-1