Big data meets big wind: A scientometric review of machine learning approaches in offshore wind energy

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-08-22 DOI:10.1016/j.egyai.2024.100418
Prangon Das , Maisha Mashiata , Gregorio Iglesias
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引用次数: 0

Abstract

Offshore wind energy offers several advantages relative to its onshore counterpart – not least stronger and steadier winds, the possibility of larger turbines, and no land occupation. The operational complexities, environmental challenges, and higher maintenance costs of offshore wind turbines necessitate innovative solutions. Traditional approaches are insufficient, and new ”big data” techniques, notably machine learning and deep learning, are poised to play a significant role in the design and optimisation of offshore wind turbines and farms. The objective of this paper is to conduct a scientometric analysis of machine learning and deep learning techniques applied to offshore wind energy. The research methodology employs a circular framework, integrating data acquisition and statistical analysis to provide a comprehensive scientometric insight into the state of the art. As regards the country of origin, most of the publications stem from just five countries, which signals a need of greater geographical diversity in this field of research. Most importantly, the rapid, steady increase in the annual number of publications since 2017 reveals the interest of the research community in this novel topic.

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大数据遇上大风能:海上风能机器学习方法的科学计量学回顾
与陆上风能相比,近海风能具有多项优势--尤其是风力更强、更稳定,可以使用更大的涡轮机,而且无需占用土地。海上风力涡轮机的运行复杂性、环境挑战和较高的维护成本要求采用创新的解决方案。传统的方法是不够的,新的 "大数据 "技术,特别是机器学习和深度学习,将在海上风力涡轮机和风电场的设计和优化中发挥重要作用。本文旨在对应用于海上风能的机器学习和深度学习技术进行科学计量分析。研究方法采用了一个循环框架,将数据采集和统计分析整合在一起,以提供对技术现状的全面科学计量学洞察。在来源国方面,大多数出版物仅来自五个国家,这表明该研究领域需要更大的地域多样性。最重要的是,自 2017 年以来,每年的出版物数量都在快速、稳定地增长,这表明了研究界对这一新颖课题的兴趣。
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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