Wind forecasting using Principal Component Analysis

Christina Skittides and Wolf-Gerrit Früh

Renewable Energy, 2014, vol. 69, issue C, 365-374

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.

Keywords: Wind energy resource; Principal Component Analysis (PCA); Forecasting (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:69:y:2014:i:c:p:365-374

DOI: 10.1016/j.renene.2014.03.068

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