No Cover Image

Journal article 88 views 14 downloads

A classification system for global wave energy resources based on multivariate clustering / Iain Fairley; Matthew Lewis; Bryson Robertson; Mark Hemer; Ian Masters; Jose Horrillo-Caraballo; Harshinie Karunarathna; Dominic Reeve

Applied Energy, Volume: 262, Start page: 114515

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

  • fairley2020.pdf

    PDF | Version of Record

    Released under the terms of a Creative Commons Attribution License (CC-BY).

    Download (12.51MB)

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...

Full description

Published in: Applied Energy
ISSN: 0306-2619
Published: Elsevier BV 2020
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa53336
Tags: Add Tag
No Tags, Be the first to tag this record!
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 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.
Keywords: Wave energy, Resource assessment, Global, Numerical model, K-means clustering
College: College of Engineering
Start Page: 114515