Artificial neural network application in short-term prediction in an oscillating water column

Show simple item record

dc.contributor.author Sheng, Wanan
dc.contributor.author Lewis, Anthony
dc.date.accessioned 2016-07-18T09:10:03Z
dc.date.available 2016-07-18T09:10:03Z
dc.date.issued 2010-01
dc.identifier.citation Sheng, W. and Lewis, A. (2010) ' Artificial neural network application in short-term prediction in an oscillating water column', Proceedings of the 20th International Offshore and Polar Engineering Conference, ISOPE 2010, Beijing; China, 20-25 June. Vol. 1, pp. 774-781. en
dc.identifier.volume 1 en
dc.identifier.startpage 774 en
dc.identifier.endpage 781 en
dc.identifier.isbn 978-1-880653-77-7
dc.identifier.issn 1098-6189
dc.identifier.uri http://hdl.handle.net/10468/2891
dc.description.abstract Oscillating Water Column (OWC) is one type of promising wave energy devices due to its obvious advantage over many other wave energy converters: no moving component in sea water. Two types of OWCs (bottom-fixed and floating) have been widely investigated, and the bottom-fixed OWCs have been very successful in several practical applications. Recently, the proposal of massive wave energy production and the availability of wave energy have pushed OWC applications from near-shore to deeper water regions where floating OWCs are a better choice. For an OWC under sea waves, the air flow driving air turbine to generate electricity is a random process. In such a working condition, single design/operation point is nonexistent. To improve energy extraction, and to optimise the performance of the device, a system capable of controlling the air turbine rotation speed is desirable. To achieve that, this paper presents a short-term prediction of the random, process by an artificial neural network (ANN), which can provide near-future information for the control system. In this research, ANN is explored and tuned for a better prediction of the airflow (as well as the device motions for a wide application). It is found that, by carefully constructing ANN platform and optimizing the relevant parameters, ANN is capable of predicting the random process a few steps ahead of the real, time with a good accuracy. More importantly, the tuned ANN works for a large range of different types of random, process. en
dc.description.sponsorship Department of Communications, Energy and Natural Resources, Ireland (Charles Parsons Initiative Program); Science Foundation Ireland (SFI Research Fellowship) en
dc.description.uri http://www.isope.org/publications/proceedings/ISOPE/ISOPE%202010/start.htm en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher The International Society of Offshore and Polar Engineers (ISOPE) en
dc.relation.uri http://www.isope.org/publications/proceedings/ISOPE/ISOPE%202010/start.htm
dc.rights © 2010 by The International Society of Offshore and Polar Engineers (ISOPE). en
dc.subject Artificial neural network en
dc.subject Oscillating water column en
dc.subject Power take-off and contro en
dc.subject Short-Term prediction en
dc.subject Wave energy converter en
dc.subject Power take-off and control en
dc.subject Wave energy en
dc.title Artificial neural network application in short-term prediction in an oscillating water column en
dc.type Conference item en
dc.internal.authorcontactother Wanan Sheng, School Of Engineering, University College Cork, Cork, Ireland. +353-21-490-3000 Email: w.sheng@ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2015-01-20T15:20:49Z
dc.description.version Published Version en
dc.internal.rssid 278757485
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder Department of Communications, Energy and Natural Resources, Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Proceedings of the International Offshore and Polar Engineering Conference en
dc.internal.copyrightchecked No. !!CORA!! en
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress w.sheng@ucc.ie en


Files in this item

This item appears in the following Collection(s)

Show simple item record

This website uses cookies. By using this website, you consent to the use of cookies in accordance with the UCC Privacy and Cookies Statement. For more information about cookies and how you can disable them, visit our Privacy and Cookies statement