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Environmental Meteorology, Albert-Ludwigs-University of Freiburg, Werthmannstrasse 10, Freiburg D-79085, Germany





Academic Editor: Frede Blaabjerg

Abstract In this paper a methodology is presented that can be used to model the annual wind energy yield AEYmod on a high spatial resolution 50 m × 50 m grid based on long-term 1979–2010 near-surface wind speed US time series measured at 58 stations of the German Weather Service DWD. The study area for which AEYmod is quantified is the German federal state of Baden-Wuerttemberg. Comparability of the wind speed time series was ensured by gap filling, homogenization and detrending. The US values were extrapolated to the height 100 m U100m,emp above ground level AGL by the Hellman power law. All U100m,emp time series were then converted to empirical cumulative distribution functions CDFemp. 67 theoretical cumulative distribution functions CDF were fitted to all CDFemp and their goodness of fit GoF was evaluated. It turned out that the five-parameter Wakeby distribution WK5 is universally applicable in the study area. Prior to the least squares boosting LSBoost-based modeling of WK5 parameters, 92 predictor variables were obtained from: i a digital terrain model DTM, ii the European Centre for Medium-Range Weather Forecasts re-analysis ERA-Interim reanalysis wind speed data available at the 850 hPa pressure level U850hPa, and iii the Coordination of Information on the Environment CORINE Land Cover CLC data. On the basis of predictor importance PI and the evaluation of model accuracy, the combination of predictor variables that provides the best discrimination between U100m,emp and the modeled wind speed at 100 m AGL U100m,mod, was identified. Results from relative PI-evaluation demonstrate that the most important predictor variables are relative elevation Φ and topographic exposure τ in the main wind direction. Since all WK5 parameters are available, any manufacturer power curve can easily be applied to quantify AEYmod. View Full-Text

Keywords: annual wind energy yield AEY; Wakeby distribution WK5; least squares boosting LSBoost; predictor importance PI; wind speed extrapolation annual wind energy yield AEY; Wakeby distribution WK5; least squares boosting LSBoost; predictor importance PI; wind speed extrapolation





Autor: Christopher Jung

Fuente: http://mdpi.com/



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