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A classification system for global wave energy resources based on multivariate clustering
Applied Energy, Volume: 262
Swansea University Authors: Iain Fairley, Ian Masters , Harshinie Karunarathna , Dominic Reeve
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DOI (Published version): 10.1016/j.apenergy.2020.114515
Abstract
Better understanding of the global wave climate is required to inform wave energy device design and large-scale deployment. Spatial variability in the global wave climate is analysed here to provide a range of characteristic design wave climates. K-means clustering was used to split the global wave...
Published in: | Applied Energy |
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ISSN: | 0306-2619 |
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2020
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URI: | https://cronfa.swan.ac.uk/Record/cronfa53336 |
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Spatial variability in the global wave climate is analysed here to provide a range of characteristic design wave climates. K-means clustering was used to split the global wave resource into 6 classes in a device agnostic, data-driven method using data from the ECMWF ERA5 reanalysis product. Classification using two sets of input data were considered: a simple set (based on significant wave height and peak wave period) and a comprehensive set including a wide range of relevant wave climate parameters. Both classifications gave resource classes with similar characteristics; 55% of tested locations were assigned to the same class. Two classes were low energy, found in enclosed seas and sheltered regions. Two classes were moderate wave energy classes; one swell dominated and the other in areas with wave action often generated by more local storms. Of the two higher energy classes; one was more often found in the northern hemisphere and the other, most energetic, predominantly on the tips of continents in the southern hemisphere. These classes match existing regional understanding of resource. Consideration of publicly available device power matrices showed good performance was primarily realised for the two highest energy resource classes (25–30% of potential deployment locations); it is suggested that effort should focus on optimising devices for additional resource classes. The authors hypothesise that the low-risk, low variability, swell dominated moderate wave energy class would be most suitable for future exploitation.</abstract><type>Journal Article</type><journal>Applied Energy</journal><volume>262</volume><journalNumber/><paginationStart/><paginationEnd/><publisher/><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0306-2619</issnPrint><issnElectronic/><keywords>Wave energy, Resource assessment, Global, Numerical model, K-means clustering</keywords><publishedDay>15</publishedDay><publishedMonth>3</publishedMonth><publishedYear>2020</publishedYear><publishedDate>2020-03-15</publishedDate><doi>10.1016/j.apenergy.2020.114515</doi><url/><notes/><college>COLLEGE NANME</college><department>Science and Engineering - Faculty</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>FGSEN</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>UKRI, EP/P008682/1</funders><lastEdited>2021-01-15T10:22:54.7720574</lastEdited><Created>2020-01-23T10:27:11.6467920</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering</level></path><authors><author><firstname>Iain</firstname><surname>Fairley</surname><order>1</order></author><author><firstname>Matthew</firstname><surname>Lewis</surname><order>2</order></author><author><firstname>Bryson</firstname><surname>Robertson</surname><order>3</order></author><author><firstname>Mark</firstname><surname>Hemer</surname><order>4</order></author><author><firstname>Ian</firstname><surname>Masters</surname><orcid>0000-0001-7667-6670</orcid><order>5</order></author><author><firstname>Jose</firstname><surname>Horrillo-Caraballo</surname><order>6</order></author><author><firstname>Harshinie</firstname><surname>Karunarathna</surname><orcid>0000-0002-9087-3811</orcid><order>7</order></author><author><firstname>Dominic</firstname><surname>Reeve</surname><orcid>0000-0003-1293-4743</orcid><order>8</order></author></authors><documents><document><filename>53336__16393__2103626cf133432491b5af6270cc6470.pdf</filename><originalFilename>fairley2020.pdf</originalFilename><uploaded>2020-01-23T10:29:22.0021313</uploaded><type>Output</type><contentLength>13119668</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>Released under the terms of a Creative Commons Attribution License (CC-BY).</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/BY/4.0/</licence></document></documents><OutputDurs/></rfc1807> |
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2021-01-15T10:22:54.7720574 v2 53336 2020-01-23 A classification system for global wave energy resources based on multivariate clustering 568e6f260489dc8139afe77757553513 Iain Fairley Iain Fairley true false 6fa19551092853928cde0e6d5fac48a1 0000-0001-7667-6670 Ian Masters Ian Masters true false 0d3d327a240d49b53c78e02b7c00e625 0000-0002-9087-3811 Harshinie Karunarathna Harshinie Karunarathna true false 3e76fcc2bb3cde4ddee2c8edfd2f0082 0000-0003-1293-4743 Dominic Reeve Dominic Reeve true false 2020-01-23 FGSEN Better understanding of the global wave climate is required to inform wave energy device design and large-scale deployment. Spatial variability in the global wave climate is analysed here to provide a range of characteristic design wave climates. K-means clustering was used to split the global wave resource into 6 classes in a device agnostic, data-driven method using data from the ECMWF ERA5 reanalysis product. Classification using two sets of input data were considered: a simple set (based on significant wave height and peak wave period) and a comprehensive set including a wide range of relevant wave climate parameters. Both classifications gave resource classes with similar characteristics; 55% of tested locations were assigned to the same class. Two classes were low energy, found in enclosed seas and sheltered regions. Two classes were moderate wave energy classes; one swell dominated and the other in areas with wave action often generated by more local storms. Of the two higher energy classes; one was more often found in the northern hemisphere and the other, most energetic, predominantly on the tips of continents in the southern hemisphere. These classes match existing regional understanding of resource. Consideration of publicly available device power matrices showed good performance was primarily realised for the two highest energy resource classes (25–30% of potential deployment locations); it is suggested that effort should focus on optimising devices for additional resource classes. The authors hypothesise that the low-risk, low variability, swell dominated moderate wave energy class would be most suitable for future exploitation. Journal Article Applied Energy 262 0306-2619 Wave energy, Resource assessment, Global, Numerical model, K-means clustering 15 3 2020 2020-03-15 10.1016/j.apenergy.2020.114515 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University UKRI, EP/P008682/1 2021-01-15T10:22:54.7720574 2020-01-23T10:27:11.6467920 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Iain Fairley 1 Matthew Lewis 2 Bryson Robertson 3 Mark Hemer 4 Ian Masters 0000-0001-7667-6670 5 Jose Horrillo-Caraballo 6 Harshinie Karunarathna 0000-0002-9087-3811 7 Dominic Reeve 0000-0003-1293-4743 8 53336__16393__2103626cf133432491b5af6270cc6470.pdf fairley2020.pdf 2020-01-23T10:29:22.0021313 Output 13119668 application/pdf Version of Record true Released under the terms of a Creative Commons Attribution License (CC-BY). true eng http://creativecommons.org/licenses/BY/4.0/ |
title |
A classification system for global wave energy resources based on multivariate clustering |
spellingShingle |
A classification system for global wave energy resources based on multivariate clustering Iain Fairley Ian Masters Harshinie Karunarathna Dominic Reeve |
title_short |
A classification system for global wave energy resources based on multivariate clustering |
title_full |
A classification system for global wave energy resources based on multivariate clustering |
title_fullStr |
A classification system for global wave energy resources based on multivariate clustering |
title_full_unstemmed |
A classification system for global wave energy resources based on multivariate clustering |
title_sort |
A classification system for global wave energy resources based on multivariate clustering |
author_id_str_mv |
568e6f260489dc8139afe77757553513 6fa19551092853928cde0e6d5fac48a1 0d3d327a240d49b53c78e02b7c00e625 3e76fcc2bb3cde4ddee2c8edfd2f0082 |
author_id_fullname_str_mv |
568e6f260489dc8139afe77757553513_***_Iain Fairley 6fa19551092853928cde0e6d5fac48a1_***_Ian Masters 0d3d327a240d49b53c78e02b7c00e625_***_Harshinie Karunarathna 3e76fcc2bb3cde4ddee2c8edfd2f0082_***_Dominic Reeve |
author |
Iain Fairley Ian Masters Harshinie Karunarathna Dominic Reeve |
author2 |
Iain Fairley Matthew Lewis Bryson Robertson Mark Hemer Ian Masters Jose Horrillo-Caraballo Harshinie Karunarathna Dominic Reeve |
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Journal article |
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Applied Energy |
container_volume |
262 |
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2020 |
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Swansea University |
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0306-2619 |
doi_str_mv |
10.1016/j.apenergy.2020.114515 |
college_str |
Faculty of Science and Engineering |
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School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering |
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description |
Better understanding of the global wave climate is required to inform wave energy device design and large-scale deployment. Spatial variability in the global wave climate is analysed here to provide a range of characteristic design wave climates. K-means clustering was used to split the global wave resource into 6 classes in a device agnostic, data-driven method using data from the ECMWF ERA5 reanalysis product. Classification using two sets of input data were considered: a simple set (based on significant wave height and peak wave period) and a comprehensive set including a wide range of relevant wave climate parameters. Both classifications gave resource classes with similar characteristics; 55% of tested locations were assigned to the same class. Two classes were low energy, found in enclosed seas and sheltered regions. Two classes were moderate wave energy classes; one swell dominated and the other in areas with wave action often generated by more local storms. Of the two higher energy classes; one was more often found in the northern hemisphere and the other, most energetic, predominantly on the tips of continents in the southern hemisphere. These classes match existing regional understanding of resource. Consideration of publicly available device power matrices showed good performance was primarily realised for the two highest energy resource classes (25–30% of potential deployment locations); it is suggested that effort should focus on optimising devices for additional resource classes. The authors hypothesise that the low-risk, low variability, swell dominated moderate wave energy class would be most suitable for future exploitation. |
published_date |
2020-03-15T04:06:14Z |
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1763753458702221312 |
score |
11.030209 |