Adaptive post-processing of short-term wind forecasts for energy applications

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Title: Adaptive post-processing of short-term wind forecasts for energy applications
Authors: Sweeney, Conor
Lynch, Peter
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Date: Apr-2011
Online since: 2011-04-08T10:54:04Z
Abstract: We present a new method of reducing the error in predicted wind speed, thus enabling better management of wind energy facilities. A numerical weather prediction model, COSMO, was used to produce 48 h forecast data every day in 2008 at horizontal resolutions of 10 and 3 km. A new adaptive statistical method was applied to the model output to improve the forecast skill. The method applied corrective weights to a set of forecasts generated using several post-processing methods. The weights were calculated based on the recent skill of the different forecasts. The resulting forecast data were compared with observed data, and skill scores were calculated to allow comparison between different post-processing methods. The total root mean square error performance of the composite forecast is superior to that of any of the individual methods.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Wiley
Journal: Wind Energy
Volume: 14
Issue: 3
Start page: 317
End page: 325
Copyright (published version): 2010 John Wiley & Sons, Ltd.
Keywords: Wind forecastingWind energyAdaptive filteringNWPStatistical post-processing
Subject LCSH: Wind forecasting
Numerical weather forecasting
Wind power
Statistical weather forecasting
DOI: 10.1002/we.420
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Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection

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