Show simple item record

dc.contributor.advisorHering, Amanda S.
dc.contributor.authorKazor, Karen E.
dc.date.accessioned2007-01-03T06:00:22Z
dc.date.accessioned2022-02-09T08:41:51Z
dc.date.available2014-11-01T04:18:44Z
dc.date.available2022-02-09T08:41:51Z
dc.date.issued2013
dc.identifierT 7381
dc.identifier.urihttps://hdl.handle.net/11124/12091
dc.description2013 Fall.
dc.descriptionIncludes illustrations (some color), color map.
dc.descriptionIncludes bibliographical references (pages 76-78).
dc.description.abstractDistinct wind conditions driven by prevailing weather patterns exist in every region around the globe. Knowledge of these conditions can be used to select and place turbines within a wind project, design controls, and build space-time models for wind forecasting. Identifying regimes quantitatively and comparing the performance of different regime identification methods are the goals of this research. The ability of statistical clustering techniques to correctly assign hourly observations to a particular regime and to select the correct number of regimes is studied through simulation. Pressure and the horizontal and vertical wind components are simulated under different regimes with a first-order Markov-switching vector autoregressive model, and the following five clustering algorithms are applied: (1) classification based on wind direction, (2) k-means, (3) a nonparametric mixture model, and (4,5) a Gaussian mixture model (GMM) with one of two covariance structures. The GMM with an unconstrained covariance matrix has the lowest misclassification rate and the highest proportion of instances in which the correct number of regimes are selected. This method is applied to one year of averaged hourly wind data observed at twenty meteorological stations. The lagged wind speed correlations between neighboring sites under upwind and downwind regimes are shown to differ substantially.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.relation.ispartof2010-2019 - Mines Theses & Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectstatistical analysis
dc.subjectclustering methods
dc.subjectwind energy
dc.subjectmixture distribution
dc.subjectMarkov model
dc.subjectforecasting
dc.subject.lcshWind forecasting
dc.subject.lcshCluster analysis
dc.subject.lcshSimulation methods
dc.subject.lcshMathematical statistics
dc.subject.lcshAlgorithms
dc.subject.lcshWind power
dc.titleStatistical identification of local and regional wind regimes
dc.typeText
dc.contributor.committeememberNavidi, William Cyrus
dc.contributor.committeememberTenorio, Luis
dcterms.embargo.terms2014-11-01
dcterms.embargo.expires2014-11-01
thesis.degree.nameMaster of Science (M.S.)
thesis.degree.levelMasters
thesis.degree.disciplineApplied Mathematics and Statistics
thesis.degree.grantorColorado School of Mines
dc.rights.access1-year embargo


Files in this item

Thumbnail
Name:
Kazor_mines_0052N_10318.pdf
Size:
10.34Mb
Format:
PDF
Description:
Statistical identification of ...

This item appears in the following Collection(s)

Show simple item record