Powering Europe - European Wind Energy Association
Powering Europe - European Wind Energy Association
Powering Europe - European Wind Energy Association
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<strong>Wind</strong>generationandwindfarms–theessentials<br />
fiGURE 12: PRobability DEnsity DistRibUtion of ERRoRs foR Day-ahEaD winD PowER foRECast foR noRth-wEst GER-<br />
Many; also shown aRE fittED GaUssian, GaMMa anD nakaGaMi DistRibUtions<br />
52<br />
NRMSE [15% of installed capacity]<br />
10 0<br />
10 -1<br />
10 -2<br />
10<br />
-5 -4 -3 -2 -1 0 1 2 3 4 5<br />
-3<br />
Error/<br />
the nakagami distribution shows the best fit for extreme forecast errors [tambke 2010].<br />
fiGURE 13: histoRiC DEVEloPMEnt of thE aVERaGE foRE-<br />
Cast ERRoR in thE wholE of GERMany anD in a sinGlE<br />
ContRol zonE in thE last ninE yEaRs<br />
NRMSE [% of installed capacity]<br />
10<br />
9<br />
8<br />
7<br />
6<br />
5<br />
4<br />
3<br />
2001<br />
day-ahead single control zone<br />
day-ahead Germany [4 zones]<br />
2002 2003 2004 2005<br />
Year<br />
2006 2007 2008 2009<br />
the improvements in accuracy are due to a combination<br />
of effects: better weather forecasts, increasing<br />
spatial distribution of installed capacity in Germany<br />
and advanced power forecast models, especially using<br />
combinations of nwPs and power forecast models<br />
[tambke 2010].<br />
RMSE [% of installed power]<br />
7<br />
6<br />
5<br />
4<br />
3<br />
2<br />
1<br />
0<br />
meas<br />
Nakagami<br />
Gamma<br />
Gaussian<br />
fiGURE 14: iMPRoVEMEnt of foRECast aCCURaCy by UsinG<br />
EnsEMblE PREDiCtions<br />
Model 1 Model 2 Model 3 Model 4 Combination<br />
Using a combination of models results in an error<br />
20% lower than using the most accurate of the single<br />
models [tambke, 2008].<br />
<strong>Powering</strong> <strong>Europe</strong>: wind energy and the electricity grid
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