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

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dc.contributor.authorSheng, Wanan
dc.contributor.authorLewis, Anthony
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderDepartment of Communications, Energy and Natural Resources, Irelanden
dc.date.accessioned2016-07-18T09:10:03Z
dc.date.available2016-07-18T09:10:03Z
dc.date.issued2010-01
dc.date.updated2015-01-20T15:20:49Z
dc.description.abstractOscillating 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.sponsorshipDepartment of Communications, Energy and Natural Resources, Ireland (Charles Parsons Initiative Program); Science Foundation Ireland (SFI Research Fellowship)en
dc.description.statusPeer revieweden
dc.description.urihttp://www.isope.org/publications/proceedings/ISOPE/ISOPE%202010/start.htmen
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationSheng, 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.endpage781en
dc.identifier.isbn978-1-880653-77-7
dc.identifier.issn1098-6189
dc.identifier.journaltitleProceedings of the International Offshore and Polar Engineering Conferenceen
dc.identifier.startpage774en
dc.identifier.urihttps://hdl.handle.net/10468/2891
dc.identifier.volume1en
dc.language.isoenen
dc.publisherThe International Society of Offshore and Polar Engineers (ISOPE)en
dc.relation.urihttp://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.subjectArtificial neural networken
dc.subjectOscillating water columnen
dc.subjectPower take-off and controen
dc.subjectShort-Term predictionen
dc.subjectWave energy converteren
dc.subjectPower take-off and controlen
dc.subjectWave energyen
dc.titleArtificial neural network application in short-term prediction in an oscillating water columnen
dc.typeConference itemen
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