 Non-linear regression model for wind turbine power curveMantas Marčiukaitis, Inga Žutautaitė, Linas Martišauskas, Benas Jokšas, Giedrius Gecevičius and Athanasios Sfetsos Renewable Energy, 2017, vol. 113, issue C, 732-741 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. Keywords: Wind energy; Wind power curve; Non-linear regression; Cross-validation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:113:y:2017:i:c:p:732-741 DOI: 10.1016/j.renene.2017.06.039 Access Statistics for this article Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides More articles in Renewable Energy from Elsevier |
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