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4 CONCLUSIONS
In this work we have presented a comparative study
of the use of variable selection in short-term wind
farm power prediction. The variable selection is done
with the Mutual Information that is estimated with the
method proposed by Kraskov. Two models are con-
sidered to study the effect of the variable selection.
One is a Multilayer Perceptron with different topolo-
gies and the other is the k-NN model for different val-
ues of the number of neighbors k.
Four different set of experiments were proposed
with different input and predicted variables. To assess
the quality of the results instead of the RMSE value,
the improvement of the RMSE over an improved per-
sistence model is used.
From the obtained results it can be concluded that
the k-NN model performs better than MLP model for
the different considered horizons and this is more em-
phasized as the number of neighbors increase. An in-
teresting conclusion is that the wind farm power pre-
diction is better done when power is used as predict-
ing variables instead of wind speed. Another fact that
the experiments has brought up and that it is in con-
sonance with the nature of the persistence model, it
is that as the horizon goes farther the MLP and k-NN
models yield better performance.
With respect to the introduction of a previous
stage of variable selection, in the experiments carried
out there is no evidence of a remarkable effect in the
results. In the MLP this effect is a bit noticeable when
predicting the wind farm power from previous mea-
sures of generated power and negligible in the k-NN
model.
ACKNOWLEDGEMENTS
This work has been partially supported by the Ca-
nary Islands government throught the project Sol-
SubC200801000137 and by the Spanish government
and FEDER through the project TIN2008-06068.
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