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Vorhersagen der Windgeschwindigkeit und Windenergie in Deutschland
[Predictions of wind speed and wind energy in Germany]

Author

Listed:
  • Daniel Ambach

    (insbesondere Statistik Europa-Universität Viadrina)

  • Robert Garthoff

    (insbesondere Statistik Europa-Universität Viadrina)

Abstract

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.

Suggested Citation

  • Daniel Ambach & Robert Garthoff, 2016. "Vorhersagen der Windgeschwindigkeit und Windenergie in Deutschland [Predictions of wind speed and wind energy in Germany]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(1), pages 15-36, February.
    • Handle: RePEc:spr:astaws:v:10:y:2016:i:1:d:10.1007_s11943-016-0177-1
    DOI: 10.1007/s11943-016-0177-1
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    References listed on IDEAS

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    Cited by:

    1. Ralf Thomas Münnich, 2016. "Vorwort des Herausgebers," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(1), pages 1-3, February.
    2. Ambach, Daniel & Schmid, Wolfgang, 2017. "A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting," Energy, Elsevier, vol. 135(C), pages 833-850.
    3. Ralf Münnich, 2016. "Vorwort des Herausgebers," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(1), pages 1-3, February.

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