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A classification system for global wave energy resources based on multivariate clustering

Iain Fairley, Matthew Lewis, Bryson Robertson, Mark Hemer, Ian Masters Orcid Logo, Jose Horrillo-Caraballo, Harshinie Karunarathna Orcid Logo, Dominic Reeve Orcid Logo

Applied Energy, Volume: 262

Swansea University Authors: Iain Fairley, Ian Masters Orcid Logo, Harshinie Karunarathna Orcid Logo, Dominic Reeve Orcid Logo

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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...

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Published in: Applied Energy
ISSN: 0306-2619
Published: 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&#x2013;30% of potential deployment locations); it is suggested that effort should focus on optimising devices for additional resource classes. 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spelling 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
format Journal article
container_title Applied Energy
container_volume 262
publishDate 2020
institution Swansea University
issn 0306-2619
doi_str_mv 10.1016/j.apenergy.2020.114515
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str 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
document_store_str 1
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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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score 11.012678

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