DocumentCode :
647889
Title :
Wave height forecasting to improve off-shore access and maintenance scheduling
Author :
Dinwoodie, Iain ; Catterson, V.M. ; McMillan, David
Author_Institution :
Inst. for Energy & Environ., Univ. of Strathclyde, Glasgow, UK
fYear :
2013
fDate :
21-25 July 2013
Firstpage :
1
Lastpage :
5
Abstract :
This paper presents research into modelling and predicting wave heights based on historical data. Wave height is one of the key criteria for allowing access to off-shore wind turbines for maintenance. Better tools for predicting wave height will allow more accurate identification of suitable “weather windows” in which access vessels can be dispatched to site. This in turn improves the ability to schedule maintenance, reducing costs related to vessel dispatch and recall due to unexpected wave patterns. The paper outlines the data available for wave height modelling. Through data mining, different modelling approaches are identified and compared. The advantages and disadvantages of each approach, and their accuracies for a given site implementation, are discussed.
Keywords :
data mining; geophysics computing; maintenance engineering; ocean waves; oceanographic techniques; offshore installations; wind turbines; access vessels; costs reducing; data mining; maintenance scheduling; off-shore access; off-shore wind turbines; wave height forecasting; wave height modelling; Artificial neural networks; Data models; Maintenance engineering; Mathematical model; Predictive models; Training; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Society General Meeting (PES), 2013 IEEE
Conference_Location :
Vancouver, BC
ISSN :
1944-9925
Type :
conf
DOI :
10.1109/PESMG.2013.6672438
Filename :
6672438
Link To Document :
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