Pipeline for Annual Averaged Wind Power Output Generation Prediction of Wind Turbines Based on Large Wind Speed Data Sets and Power Curve Data.

Data Analysis Energy Analysis Power Curve Modeling Power Output Generation Wind Energy Wind Speed Probability Distributions

Journal

MethodsX
ISSN: 2215-0161
Titre abrégé: MethodsX
Pays: Netherlands
ID NLM: 101639829

Informations de publication

Date de publication:
2021
Historique:
received: 09 09 2020
revised: 29 05 2021
accepted: 23 08 2021
entrez: 10 11 2021
pubmed: 11 11 2021
medline: 11 11 2021
Statut: epublish

Résumé

In this article, an abstract framework for annual averaged wind power output generation prediction of wind turbines is presented which is heavily based on large wind speed data sets and power curve data of wind turbines due to the rising interest in wind energy as one main future renewable energy source. As combinations of arbitrary power curve modeling techniques and arbitrary wind speed distributions based on wind speed data are seldom combined, the abstract combination of these two aspects in wind power output generation prediction in one pipeline is thoroughly described here. Conclusively, one detailed example wind speed data set from a weather station situation in Bremen, Germany illustrates applicability of the presented framework.

Identifiants

pubmed: 34754770
doi: 10.1016/j.mex.2021.101499
pii: S2215-0161(21)00292-2
pmc: PMC8563481
doi:

Types de publication

Journal Article

Langues

eng

Pagination

101499

Informations de copyright

© 2021 The Authors. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

The authors declare that they have no potential conflict of interest.

Auteurs

Benjamin Wacker (B)

Department of Engineering and Natural Sciences, University of Applied Sciences Merseburg, Eberhard-Leibnitz-Str. 2, Merseburg D-06217, Germany.
Next Generation Mobility Group, Max-Planck-Institute for Dynamics and Self-Organization, Department of Dynamics of Complex Fluids, Am Fassberg 17, Göttingen D-37077, Germany.

Jan Chr Schlüter (JC)

Next Generation Mobility Group, Max-Planck-Institute for Dynamics and Self-Organization, Department of Dynamics of Complex Fluids, Am Fassberg 17, Göttingen D-37077, Germany.
Institute for Dynamics of Complex Systems, Faculty of Physics, Georg-August-University of Göttingen, Friedrich-Hund-Platz 1, Göttingen D-37077, Germany.
Flexible Transport Systems and Complex Urban Dynamics Research Group, "Friedrich List" Faculty of Transport and Traffic Sciences, Technical University of Dresden, Hettnerstr. 1, 01069 Dresden, Germany.
Econophysics Lab, Chair for Network Dynamics, Center for Advancing Electronics Dresden (cfaed), Technical University of Dresden, Helmholtzstr. 18, 01069 Dresden, Germany.

Classifications MeSH