Y. Ren, P. N. Suganthan

prediction. The second configuration has combined all IMFs and residue together to form a feature vector set for

computing the distance matrix and the prediction has followed the conventional kNN model. The two configura-

tions have been compared with the persistent model and the kNN model with a wind speed TS recorded in Sin-

gapore. The results have shown that the two configurations outperformed the persistent model and kNN for

longer term forecasting. The second configuration has outperformed the first configuration for 1 and 3 step-

ahead forecasting.

For future work, a possible improvement is on the feature vector selection. Some statistical methods can be

applied for the dimension selection instead of user-defined range followed by grid search based on cross valida-

tion performances. Another possible future work is to apply different weight scheme w to the distance matrix

creation stage. Instead of a uniform w, a linear or exponential decayed w can be used to weigh the distance. This

weighted distance may improve the selection of nearest neighbors.

Acknowledgements

The author Ren Ye would like to thank National Research Foundation (NRF) for providing the Clean Energy

Program (CEPO) research scholarship.

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