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 and Dominic E. Reeve

Applied Energy, 2020, vol. 262, issue C, No S0306261920300271

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 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (31)

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DOI: 10.1016/j.apenergy.2020.114515

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