Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/57200
Type of publication: Straipsnis Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or / and Scopus (S1)
Field of Science: Energetika ir termoinžinerija / Energy and thermoengineering (T006);Matematika / Mathematics (N001)
Author(s): Marčiukaitis, Mantas;Žutautaitė, Inga;Martišauskas, Linas;Jokšas, Benas;Gecevičius, Giedrius;Sfetsos, Athanasios
Title: Non-linear regression model for wind turbine power curve
Is part of: Renewable energy. Oxford : Pergamon-Elsevier Science, 2017, Vol. 113
Extent: p. 732-741
Date: 2017
Keywords: Wind energy;Wind power curve;Non-linear regression;Cross-validation
Abstract: In this article, a study of wind turbine power curve modelling is presented with application to a particular wind turbine of Seirijai wind farm in Lithuania. A non-linear regression model for wind turbine power curve approximation was proposed, which stands out with several advantages, such as fitting physical properties of wind turbine (i.e., power curve does not exceed the highest value of generated power as it is maximum physically possible), lower number of parameters to be estimated, dependency on only one factor. MAPE was used as a measure of approximation method accuracy. Mode approach was introduced as an alternative to typical techniques for modelling power curves of wind turbines with the aim to avoid elimination of the outliers from initial data and the impact of varying concentration of observations in the full range of wind speed. Performed cross-validation analysis demonstrated that the developed power curve model is appropriate for the prediction of wind power and is not directly dependent on the initial data set
Internet: https://doi.org/10.1016/j.renene.2017.06.039
https://doi.org/10.1016/j.renene.2017.06.039
Affiliation(s): Informatikos fakultetas
Lietuvos energetikos institutas
Matematikos ir statistikos katedra
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

Files in This Item:
marc.xml10.1 kBXMLView/Open

MARC21 XML metadata

Show full item record

Page view(s)

158
checked on Dec 9, 2019

Download(s)

12
checked on Dec 9, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.