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Activity Number: 410
Type: Topic Contributed
Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #313564
Title: Spatiotemporal Analysis and Clustering of Wind Speeds
Author(s): Laura Tupper*+ and David Scott Matteson
Companies: Cornell University and Cornell University
Keywords: clustering ; wind ; renewable energy ; spatiotemporal data ; short-time Fourier transforms
Abstract:

We explore the behavior of wind speed across time and multiple locations, using the Eastern Wind Dataset published by the National Renewable Energy Laboratory. This dataset gives wind speeds over three years at hundreds of potential wind farm sites. Analysis of wind speed is necessary to the integration of wind energy into the power grid; short-term variability in wind speed affects decisions about usage of other power sources, making the shape of the wind speed curve as important as the level. To assess differences in wind behavior across days, we adapt a functional distance measure, the band distance, based on the band depth of Lopez-Pintado and Romo. This measure emphasizes the shape of the wind speed curve relative to other observations, and can be used to cluster days without reliance on pointwise Euclidean distance. To emphasize short-term variability, we examine the short-time Fourier transform of the nonstationary speed time series; we can also adjust for seasonal effects, and use these transforms as input for the band distance. We show that this approach to characterizing differing wind behavior reveals patterns not shown by standard clustering methods, such as k-means.


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