Wind Energy Science
Wind Energy Science
WES
Articles | Volume 5, issue 2
Articles | Volume 5, issue 2
https://doi.org/10.5194/wes-5-601-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/wes-5-601-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 5, issue 2
Research article
 | 
26 May 2020
Research article |  | 26 May 2020

Analysing uncertainties in offshore wind farm power output using measure–correlate–predict methodologies

Michael Denis Mifsud, Tonio Sant, and Robert Nicholas Farrugia

Viewed

Total article views: 2,153 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,383 718 52 2,153 52 44
  • HTML: 1,383
  • PDF: 718
  • XML: 52
  • Total: 2,153
  • BibTeX: 52
  • EndNote: 44
Views and downloads (calculated since 02 Dec 2019)
Cumulative views and downloads (calculated since 02 Dec 2019)

Viewed (geographical distribution)

Total article views: 2,153 (including HTML, PDF, and XML) Thereof 1,725 with geography defined and 428 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 24 Apr 2024
Download
Short summary
In offshore wind, it is important to have an accurate wind resource assessment. Measure–correlate–predict (MCP) is a statistical method used in the assessment of the wind resource at a candidate site. Being a statistical method, it is subject to uncertainty, resulting in an uncertainty in the power output from the wind farm. This study involves the use of wind data from the island of Malta and uses a hypothetical wind farm to establish the best MCP methodology for the wind resource assessment.
Read more
Special issue
Wind Energy Science Conference 2019
Altmetrics
Final-revised paper
Preprint