Abstract:
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We explore the unsupervised classification of spatio-temporal data, with an application in wind power generation. Clustering can be used as a data-driven way to characterize the behavior of a dataset, revealing patterns and allowing the selection of representatives to characterize the space, but it relies on the selection of a useful measure of similarity between observations. Previous work has used the band distance, a depth-based distance metric for high-dimensional vector data, to cluster time series of wind speeds. We now extend the depth-based approach to take into account wind speeds at multiple locations, and compare the results of simple concatenation, dimension reduction of the multivariate data using spatial techniques, and a similarity measure based on the full matrix of times and locations.
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