A spatial-temporal clustering for low ocean renewable energy resources using K-means clustering

Mat Nizam Uti, Ami Hassan Md Din, Norhakim Yusof and Omar Yaakob

Renewable Energy, 2023, vol. 219, issue P2

Abstract: Advancements in space technology have enabled the acquisition of reliable marine data, facilitating research on the potential of ocean renewable energy as an alternative source that can reduce dependency on fossil fuels, subsequently mitigating climate change. However, the ocean renewable energy development in Malaysia has not received adequate attention from local authorities and communities due to low resources in this area. Insufficient in-situ data, both in spatial and temporal dimensions, poses challenges in investigating the characteristics of ocean parameters, hindering a thorough study of the potential for ocean renewable energy development. Hence, this paper aims to identify potential ocean renewable energy development locations using the altimetry data and spatial-temporal clustering using the K-means technique. Theoretically, Malaysian seas are suitable for harnessing wind and waves with energy density ranges of up to 104.69 kW/m2 and 4.21 kW/m, respectively. This study enhances the understanding of Malaysian potential for ocean renewable energy, providing valuable information to stakeholders and the government to increase their interest in ocean renewable energy as a sustainable source for electricity generation in the future.

Keywords: Spatial-temporal clustering; K-means; Ocean renewable energy; Satellite altimeter; Wave and wind (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:219:y:2023:i:p2:s0960148123014647

DOI: 10.1016/j.renene.2023.119549

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