Journal cover Journal topic
Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
Journal topic
Discussion papers
https://doi.org/10.5194/wes-2019-92
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2019-92
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 02 Dec 2019

Submitted as: research article | 02 Dec 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Wind Energy Science (WES).

Analysing Uncertainties in Offshore Wind Farm Power Output using Measure Correlate Predict Methodologies

Michael Denis Mifsud1, Tonio Sant2, and Robert Nicholas Farrugia1 Michael Denis Mifsud et al.
  • 1Institute for Sustainable Energy, University of Malta, Marsaxlokk, MXK1351 Malta
  • 2Department of Mechanical Engineering, University of Malta, Msida, MSD2080, Malta

Abstract. This paper investigates the uncertainties resulting from different Measure-Correlate-Predict methods to project the power and energy yield from a wind farm. The analysis is based on a case study that utilizes short-term data acquired from a LiDAR wind measurement system deployed at a coastal site in the northern part of the island of Malta and long-term measurements from the island’s international airport. The wind speed at the candidate site is measured by means of a LiDAR system. The predicted power output for a hypothetical offshore wind farm from the various MCP methodologies is compared to the actual power output obtained directly from the input of LiDAR data to establish which MCP methodology best predicts the power generated.

The power output from the wind farm is predicted by inputting wind speed and direction derived from the different MCP methods into windPRO® (https://www.emd.dk/windpro). The predicted power is compared to the power output generated from the actual wind and direction data by using the Mean Squared Error (MSE) and the Mean Absolute Error (MAE) measures. This methodology will establish which combination of MCP methodology and wind farm configuration will have the least prediction error.

The best MCP methodology which combines prediction of wind speed and wind direction, together with the topology of the wind farm, is that using Artificial Neural Networks. However, the study concludes that the other MCP methodologies cannot be discarded as it is always best to compare different combinations of MCP methodologies for wind speed and wind direction, together with different wake models and wind farm topologies.

Michael Denis Mifsud et al.
Interactive discussion
Status: open (until 13 Jan 2020)
Status: open (until 13 Jan 2020)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Michael Denis Mifsud et al.
Michael Denis Mifsud et al.
Viewed  
Total article views: 90 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
69 19 2 90 0 0
  • HTML: 69
  • PDF: 19
  • XML: 2
  • Total: 90
  • BibTeX: 0
  • EndNote: 0
Views and downloads (calculated since 02 Dec 2019)
Cumulative views and downloads (calculated since 02 Dec 2019)
Viewed (geographical distribution)  
Total article views: 61 (including HTML, PDF, and XML) Thereof 60 with geography defined and 1 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 09 Dec 2019
Publications Copernicus
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 windfarm. This study involves the use of wind data from the Island of Malta and uses a hypothetical windfarm to establish the best MCP methodology for the wind resource assessment.
In offshore wind, it is important to have an accurate wind resource assessment. ...
Citation