Big data meets big wind: A scientometric review of machine learning approaches in offshore wind energy
Prangon Das, Maisha Mashiata, Gregório Iglesias
Abstract
• Overall view of machine learning and deep learning in offshore wind energy. • The number of publications per annum has been increasing steadily since 2017. • The countries with the most publications are China, the U.K., Spain, Germany and the U.S. • The scientometric analysis revealed the research networks through co-authorship of papers. 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.