Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms

Handle URI:
http://hdl.handle.net/10754/552382
Title:
Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms
Authors:
Lee, Giwhyun; Ding, Yu; Genton, Marc G. ( 0000-0001-6467-2998 ) ; Xie, Le
Abstract:
In the wind industry, a power curve refers to the functional relationship between the power output generated by a wind turbine and the wind speed at the time of power generation. Power curves are used in practice for a number of important tasks including predicting wind power production and assessing a turbine’s energy production efficiency. Nevertheless, actual wind power data indicate that the power output is affected by more than just wind speed. Several other environmental factors, such as wind direction, air density, humidity, turbulence intensity, and wind shears, have potential impact. Yet, in industry practice, as well as in the literature, current power curve models primarily consider wind speed and, sometimes, wind speed and direction. We propose an additive multivariate kernel method that can include the aforementioned environmental factors as a new power curve model. Our model provides, conditional on a given environmental condition, both the point estimation and density estimation of power output. It is able to capture the nonlinear relationships between environmental factors and the wind power output, as well as the high-order interaction effects among some of the environmental factors. Using operational data associated with four turbines in an inland wind farm and two turbines in an offshore wind farm, we demonstrate the improvement achieved by our kernel method.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms 2015, 110 (509):56 Journal of the American Statistical Association
Publisher:
Informa UK Limited
Journal:
Journal of the American Statistical Association
Issue Date:
22-Apr-2015
DOI:
10.1080/01621459.2014.977385
Type:
Article
ISSN:
0162-1459; 1537-274X
Additional Links:
http://www.tandfonline.com/doi/full/10.1080/01621459.2014.977385
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorLee, Giwhyunen
dc.contributor.authorDing, Yuen
dc.contributor.authorGenton, Marc G.en
dc.contributor.authorXie, Leen
dc.date.accessioned2015-05-06T13:27:59Zen
dc.date.available2015-05-06T13:27:59Zen
dc.date.issued2015-04-22en
dc.identifier.citationPower Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms 2015, 110 (509):56 Journal of the American Statistical Associationen
dc.identifier.issn0162-1459en
dc.identifier.issn1537-274Xen
dc.identifier.doi10.1080/01621459.2014.977385en
dc.identifier.urihttp://hdl.handle.net/10754/552382en
dc.description.abstractIn the wind industry, a power curve refers to the functional relationship between the power output generated by a wind turbine and the wind speed at the time of power generation. Power curves are used in practice for a number of important tasks including predicting wind power production and assessing a turbine’s energy production efficiency. Nevertheless, actual wind power data indicate that the power output is affected by more than just wind speed. Several other environmental factors, such as wind direction, air density, humidity, turbulence intensity, and wind shears, have potential impact. Yet, in industry practice, as well as in the literature, current power curve models primarily consider wind speed and, sometimes, wind speed and direction. We propose an additive multivariate kernel method that can include the aforementioned environmental factors as a new power curve model. Our model provides, conditional on a given environmental condition, both the point estimation and density estimation of power output. It is able to capture the nonlinear relationships between environmental factors and the wind power output, as well as the high-order interaction effects among some of the environmental factors. Using operational data associated with four turbines in an inland wind farm and two turbines in an offshore wind farm, we demonstrate the improvement achieved by our kernel method.en
dc.publisherInforma UK Limiteden
dc.relation.urlhttp://www.tandfonline.com/doi/full/10.1080/01621459.2014.977385en
dc.rightsThis is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on Apr. 22, 2015, available online: http://wwww.tandfonline.com/10.1080/01621459.2014.977385.en
dc.subjectAdditive multivariate kernel regressionen
dc.subjectNonparametric estimationen
dc.subjectTurbine performance assessmenten
dc.subjectWind power forecasten
dc.titlePower Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farmsen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalJournal of the American Statistical Associationen
dc.eprint.versionPost-printen
dc.contributor.institutionKorea Army Academy, Yeongcheon, Republic of Koreaen
dc.contributor.institutionIndustrial and Systems Engineering, Texas A&M University, College Station, TX 77843-3131en
dc.contributor.institutionElectrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3131en
kaust.authorGenton, Marc G.en
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