Advanced search
1 file | 1.67 MB

Data-driven multivariate power curve modeling of offshore wind turbines

Author
Organization
Keywords
Condition monitoring, Performance monitoring, Machine learning, Data mining, Wind energy

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 1.67 MB

Citation

Please use this url to cite or link to this publication:

Chicago
Janssens, Olivier, Nymfa Noppe, Christof Devriendt, Rik Van de Walle, and Sofie Van Hoecke. 2016. “Data-driven Multivariate Power Curve Modeling of Offshore Wind Turbines.” Engineering Applications of Artificial Intelligence 55: 331–338.
APA
Janssens, O., Noppe, N., Devriendt, C., Van de Walle, R., & Van Hoecke, S. (2016). Data-driven multivariate power curve modeling of offshore wind turbines. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 55, 331–338.
Vancouver
1.
Janssens O, Noppe N, Devriendt C, Van de Walle R, Van Hoecke S. Data-driven multivariate power curve modeling of offshore wind turbines. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE. OXFORD: PERGAMON-ELSEVIER SCIENCE LTD; 2016;55:331–8.
MLA
Janssens, Olivier, Nymfa Noppe, Christof Devriendt, et al. “Data-driven Multivariate Power Curve Modeling of Offshore Wind Turbines.” ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 55 (2016): 331–338. Print.
@article{8121565,
  author       = {Janssens, Olivier and Noppe, Nymfa and Devriendt, Christof and Van de Walle, Rik and Van Hoecke, Sofie},
  issn         = {0952-1976},
  journal      = {ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE},
  language     = {eng},
  pages        = {331--338},
  publisher    = {PERGAMON-ELSEVIER SCIENCE LTD},
  title        = {Data-driven multivariate power curve modeling of offshore wind turbines},
  url          = {http://dx.doi.org/10.1016/j.engappai.2016.08.003},
  volume       = {55},
  year         = {2016},
}

Altmetric
View in Altmetric
Web of Science
Times cited: