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Martins, A. A. A. F., Vaz, D. C., Silva, T.A.N., Cardoso, M. G. M. S. & Carvalho, A. (2023). AN APPLICATION OF TIME SERIES CLUSTERING USING A COMBINED DISTANCE.   6th International Conference on Numerical and Symbolic Computation  Developments and Applications.

A. A. Martins et al.,  "AN APPLICATION OF TIME SERIES CLUSTERING USING A COMBINED DISTANCE", in   6th Int. Conf. on Numerical and Symbolic Computation  Developments and Applications, Évora, 2023

@misc{martins2023_1734874376970,
	author = "Martins, A. A. A. F. and Vaz, D. C. and Silva, T.A.N. and Cardoso, M. G. M. S. and Carvalho, A.",
	title = "AN APPLICATION OF TIME SERIES CLUSTERING USING A COMBINED DISTANCE",
	year = "2023",
	url = "https://www.symcomp2023.uevora.pt/"
}

TY  - CPAPER
TI  - AN APPLICATION OF TIME SERIES CLUSTERING USING A COMBINED DISTANCE
T2  -   6th International Conference on Numerical and Symbolic Computation  Developments and Applications
AU  - Martins, A. A. A. F.
AU  - Vaz, D. C.
AU  - Silva, T.A.N.
AU  - Cardoso, M. G. M. S.
AU  - Carvalho, A.
PY  - 2023
CY  - Évora
UR  - https://www.symcomp2023.uevora.pt/
AB  - Clustering time series aims at uncovering diverse longitudinal patterns. In this study we analyse time series of various parameters collected atop wind turbines in a wind farm in Portugal. Considering wind data in clustering may reveal differences in operation between neighbouring turbines, due to their position relative to one another and to terrain features. In this work, we use an approach to wind speed time series clustering based on a convex combination of distance measures between time series. For visualizing the resulting groups, we propose a graphical representation, Distance Matrix, which is quite more informative than the classical Multidimensional Scaling (MDS) map. This representation allows for quick comparisons between pairs of turbines for (dis)similarities. Our approach provides distinct insights regarding the differences between time series, emphasizing differences in values (Euclidean distance), trends (Pearson-based distance), and cyclical behaviours (Euclidean distance between periodograms and/or autocorrelation structures). In most cases, we found two groups, which were not always coincident with the geographical groups, but the proposed approach could also find the rationale behind the clusters that were formed. The results obtained may help identifying undesirable aerodynamic loads that the blades of a particular wind turbine may be subjected to, thereby shortening its time in-service.
ER  -