Data-driven analysis of reliability, accessibility and survivability in marine renewable energy projects

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dc.contributor.advisor Murphy, James en
dc.contributor.advisor Lewis, Tony en
dc.contributor.author Barker, Aaron
dc.date.accessioned 2019-07-18T11:04:05Z
dc.date.available 2019-07-18T11:04:05Z
dc.date.issued 2019
dc.date.submitted 2019
dc.identifier.citation Barker, A. 2019. Data-driven analysis of reliability, accessibility and survivability in marine renewable energy projects. PhD Thesis, University College Cork. en
dc.identifier.endpage 408 en
dc.identifier.uri http://hdl.handle.net/10468/8197
dc.description.abstract Increased activity in the Marine Renewable Energy industry has driven the need for an improved understanding of the wave climate and wave energy resource, which are fundamental to the development of any marine energy project. This thesis assesses the characterisation of the wave energy resource available at the Killard Point site in Co. Clare, as part of a joint industry project on the Electricity Supply Board (ESB)’s’s WestWave project, Ireland’s first proposed commercial wave energy installation. This assessment is done with an eye on the newly formed International Electrotechnical Commission standards for metocean resource assessment, with a focus on producing a standardised analysis method which informs the extractable wave energy resource. Many existing practices are questioned, and their merits assessed. This thesis adds novel tools and advanced data analysis methods, which are implemented to develop new methodologies for enhancing our understanding of our wave resource, and which subsequently enable improved assessment of the impacts of reliability, accessibility and survivability of Marine Renewable Energy projects. The impact of spectral shape on device energy production is examined using both a theoretical and practical application, to show the disconnect between currently accepted practices and the level of certainty which will be required to drive commercial success. A new methodology for the assessment of extreme wave conditions is developed, while a large contribution of this thesis is in developing and applying machine learning techniques to enhance the accuracy and dependability of wave parameter relationships and the prediction of device energy production by improving the estimation of absent wave data. This approach has been shown to result in a reduction in power production error at Killard Point from 30% to just 3.5%. This novel Machine Learning method is integral in enabling the level of characterisation that will be necessary for the commercial success of Marine Renewable Energy projects. The major contribution of this thesis is the development of an enhanced understanding of the available wave resource at the Killard Point site; producing a numerical hindcast nearshore wave model which attempts to bring the project to the level required by IEC standards, while addressing technical issues which affect the standardisation, accuracy, usability and predictability of the data gathered. This work does not focus on the Marine Renewable Energy technology in use, nor will it explore in great detail the economic vagaries of MRE projects. Instead, it focusses on developing methods which will provide a large missing piece of the puzzle in MRE development, accurate and dependable metocean analysis. The results presented here have wider applicability, and indeed much of this research has taken place, or has been verified at, other sites along the west-coast of Ireland.   en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher University College Cork en
dc.rights © 2019, Aaron Barker. en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/ en
dc.subject Technoeconomics en
dc.subject Wave energy en
dc.subject Renewable energy en
dc.subject Data analysis en
dc.title Data-driven analysis of reliability, accessibility and survivability in marine renewable energy projects en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PhD en
dc.internal.availability Full text available en
dc.check.info Not applicable en
dc.description.version Accepted Version
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder Electricity Supply Board en
dc.description.status Not peer reviewed en
dc.internal.school Civil and Environmental Engineering en
dc.check.type No Embargo Required
dc.check.reason Not applicable en
dc.check.opt-out Not applicable en
dc.thesis.opt-out false
dc.check.embargoformat Embargo not applicable (If you have not submitted an e-thesis or do not want to request an embargo) en
ucc.workflow.supervisor jimmy.murphy@ucc.ie
dc.internal.conferring Summer 2019 en
dc.internal.ricu Centre for Marine Renewable Energy Ireland (MaREI) en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2302/IE/Marine Renewable Energy Ireland (MaREI) - The SFI Centre for Marine Renewable Energy Research/ en


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