Band Depth Clustering for Nonstationary Time Series and Wind Speed Behavior. Issue 2 (3rd April 2018)

• Record Type: Journal Article
• Authors: Tupper, Laura L.; Matteson, David S.; Anderson, C. Lindsay; Zephyr, Luckny
• Is Part Of: Technometrics. Volume 60:Issue 2(2018)
• Page Start: 245
• Page End: 254
• Publication Date: 2018-04-03
• Subjects: Cluster analysis -- Depth statistics -- Distance metrics -- Time series analysis -- Wind power; Statistical physics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; Engineering -- Statistical methods -- Periodicals; 519.5
• DOI: 10.1080/00401706.2017.1345700
• Languages: English
• ISSNs: 0040-1706

Abstract

ABSTRACT: We explore the behavior of wind speed over time, using a subset of the Eastern Wind Dataset published by the National Renewable Energy Laboratory. This dataset gives modeled wind speeds over three years at hundreds of potential wind farm sites. Wind speed analysis 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, so that the shape of the wind speed time series becomes as important as the overall level. To assess differences in intra-day time series, we propose a functional distance measure, the band distance, which extends the band depth of López-Pintado and Romo. This measure emphasizes the shape of time series or functional observations relative to other members of a dataset and allows clustering of observations without reliance on pointwise Euclidean distance. We show a method for adjusting for seasonal effects in wind speed, and use these standardizations as input for the band distance. We demonstrate the utility of the new method in simulation studies and an application to the MOST power grid algorithm, where the band distance improves reliability over standard methods at a comparable cost.