Handle URI:
http://hdl.handle.net/10754/599353
Title:
Powering Up With Space-Time Wind Forecasting
Authors:
Hering, Amanda S.; Genton, Marc G.
Abstract:
The technology to harvest electricity from wind energy is now advanced enough to make entire cities powered by it a reality. High-quality, short-term forecasts of wind speed are vital to making this a more reliable energy source. Gneiting et al. (2006) have introduced a model for the average wind speed two hours ahead based on both spatial and temporal information. The forecasts produced by this model are accurate, and subject to accuracy, the predictive distribution is sharp, that is, highly concentrated around its center. However, this model is split into nonunique regimes based on the wind direction at an offsite location. This paper both generalizes and improves upon this model by treating wind direction as a circular variable and including it in the model. It is robust in many experiments, such as predicting wind at other locations. We compare this with the more common approach of modeling wind speeds and directions in the Cartesian space and use a skew-t distribution for the errors. The quality of the predictions from all of these models can be more realistically assessed with a loss measure that depends upon the power curve relating wind speed to power output. This proposed loss measure yields more insight into the true value of each models predictions. © 2010 American Statistical Association.
Citation:
Hering AS, Genton MG (2010) Powering Up With Space-Time Wind Forecasting. Journal of the American Statistical Association 105: 92–104. Available: http://dx.doi.org/10.1198/jasa.2009.ap08117.
Publisher:
Informa UK Limited
Journal:
Journal of the American Statistical Association
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Mar-2010
DOI:
10.1198/jasa.2009.ap08117
Type:
Article
ISSN:
0162-1459; 1537-274X
Sponsors:
Amanda S Hering is Assistant Professor, Department of Mathematical and Computer Sciences, Colorado School of Mines. Golden. CO 80401-1887 (E-mail ahering@nunes edit) Marc G Genton is Professor. Department of Statistics. Texas A&M University. College Station, TX 77843-3143 (E-mail genton@stat tamu edu) This research was partially supported by NSF grants DMS-0504896. CMG ATM-0620624. and Award No KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST) The author, would like to thank the Editor and two referees for their helpful comments and suggestions. as well as Tilmann Gneiting for providing the data and computer code for the RSTD model Stel Walker of Oregon State University's Energy Resources Research Laboratory and Bonneville Power Administration provided the 10-minute data We also thank Michael Stein for helpful comments made on an earlier version of this work
Appears in Collections:
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Full metadata record

DC FieldValue Language
dc.contributor.authorHering, Amanda S.en
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2016-02-28T05:49:51Zen
dc.date.available2016-02-28T05:49:51Zen
dc.date.issued2010-03en
dc.identifier.citationHering AS, Genton MG (2010) Powering Up With Space-Time Wind Forecasting. Journal of the American Statistical Association 105: 92–104. Available: http://dx.doi.org/10.1198/jasa.2009.ap08117.en
dc.identifier.issn0162-1459en
dc.identifier.issn1537-274Xen
dc.identifier.doi10.1198/jasa.2009.ap08117en
dc.identifier.urihttp://hdl.handle.net/10754/599353en
dc.description.abstractThe technology to harvest electricity from wind energy is now advanced enough to make entire cities powered by it a reality. High-quality, short-term forecasts of wind speed are vital to making this a more reliable energy source. Gneiting et al. (2006) have introduced a model for the average wind speed two hours ahead based on both spatial and temporal information. The forecasts produced by this model are accurate, and subject to accuracy, the predictive distribution is sharp, that is, highly concentrated around its center. However, this model is split into nonunique regimes based on the wind direction at an offsite location. This paper both generalizes and improves upon this model by treating wind direction as a circular variable and including it in the model. It is robust in many experiments, such as predicting wind at other locations. We compare this with the more common approach of modeling wind speeds and directions in the Cartesian space and use a skew-t distribution for the errors. The quality of the predictions from all of these models can be more realistically assessed with a loss measure that depends upon the power curve relating wind speed to power output. This proposed loss measure yields more insight into the true value of each models predictions. © 2010 American Statistical Association.en
dc.description.sponsorshipAmanda S Hering is Assistant Professor, Department of Mathematical and Computer Sciences, Colorado School of Mines. Golden. CO 80401-1887 (E-mail ahering@nunes edit) Marc G Genton is Professor. Department of Statistics. Texas A&M University. College Station, TX 77843-3143 (E-mail genton@stat tamu edu) This research was partially supported by NSF grants DMS-0504896. CMG ATM-0620624. and Award No KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST) The author, would like to thank the Editor and two referees for their helpful comments and suggestions. as well as Tilmann Gneiting for providing the data and computer code for the RSTD model Stel Walker of Oregon State University's Energy Resources Research Laboratory and Bonneville Power Administration provided the 10-minute data We also thank Michael Stein for helpful comments made on an earlier version of this worken
dc.publisherInforma UK Limiteden
dc.subjectCircular variableen
dc.subjectPower curveen
dc.subjectSkew-distributionen
dc.subjectWind directionen
dc.subjectWind speeden
dc.titlePowering Up With Space-Time Wind Forecastingen
dc.typeArticleen
dc.identifier.journalJournal of the American Statistical Associationen
dc.contributor.institutionColorado School of Mines, Golden, United Statesen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en
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