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Wind forecasting using Principal Component Analysis

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  • Skittides, Christina
  • Früh, Wolf-Gerrit

Abstract

We present a new statistical wind forecasting tool based on Principal Component Analysis (PCA), which is trained on past data to predict the wind speed using an ensemble of dynamically similar past events. At the same time the method provides a prediction of the likely forecasting error. The method is applied to Meteorological Office wind speed and direction data from a site in Edinburgh. For the training period, the years 2008–2009 were used, and the wind forecasting was tested for the data from 2010 for that site. Different parameter values were also used in the PCA analysis to explore the sensitivity analysis of the results.

Suggested Citation

  • Skittides, Christina & Früh, Wolf-Gerrit, 2014. "Wind forecasting using Principal Component Analysis," Renewable Energy, Elsevier, vol. 69(C), pages 365-374.
    • Handle: RePEc:eee:renene:v:69:y:2014:i:c:p:365-374
    DOI: 10.1016/j.renene.2014.03.068
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    Cited by:

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    13. Wang, Jianzhou & Heng, Jiani & Xiao, Liye & Wang, Chen, 2017. "Research and application of a combined model based on multi-objective optimization for multi-step ahead wind speed forecasting," Energy, Elsevier, vol. 125(C), pages 591-613.
    14. Sandra Minerva Valdivia-Bautista & José Antonio Domínguez-Navarro & Marco Pérez-Cisneros & Carlos Jesahel Vega-Gómez & Beatriz Castillo-Téllez, 2023. "Artificial Intelligence in Wind Speed Forecasting: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
    15. Liu, Da & Wang, Jilong & Wang, Hui, 2015. "Short-term wind speed forecasting based on spectral clustering and optimised echo state networks," Renewable Energy, Elsevier, vol. 78(C), pages 599-608.
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