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AI-Empowered Marine Energy
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AI-Empowered Marine Energy

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: 10 July 2025 | Viewed by 4797

Special Issue Editors

Prof. Dr. Zhaolong Han

E-Mail Website
Guest Editor
School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China
Interests: offshore wind turbines; ocean energy; fluid–structure interaction; wind engineering
Prof. Dr. Jianhua Zhang

E-Mail Website
Guest Editor
College of Aerospace and Civil Engineering, Harbin Engineering University, Harbin 150001, China
Interests: offshore wind turbines; offshore floating photovoltaic systems; offshore platform structures; structural dynamics
Special Issues, Collections and Topics in MDPI journals
Dr. Zhongqiang Liu

E-Mail Website
Guest Editor
Norwegian Geotechnical Institute, Ullevaal Stadion, P.O. Box 3930, NO-0806 Oslo, Norway
Interests: offshore foundations; offshore wind; machine learning; reliability-based design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, much progress has been made in the field of offshore renewable energy, which is beneficial to the realization of a global energy green transformation and low-carbon development. However, the random fluctuations in renewable energy and the complex marine environments have limited the use of Marine Renewable Energy in the open sea. At present, the rapid development of artificial intelligence (AI) technology covers many aspects such as data analysis and prediction models. It has shown great applicability potential in the field of marine energy and is expected to promote newer developments in marine energy. Based on this, this Special Issue is expected to cover the latest achievements in the field of artificial intelligence technology and the comprehensive development of marine energy. Research topics include but are not limited to the application of AI in the design of new marine energy systems, optimization, and operation, as well as the AI-driven integration of offshore energy and marine resource industries. Reviews or research papers in related fields are also welcome.

Background: In recent years, much progress has been made in the field of offshore renewable energy, which is beneficial to the realization of a global energy green transformation and low-carbon development. Artificial intelligence technology has shown great application potential in the field of marine energy and is expected to promote new developments of marine energy.

Aim and Scope: This Special Issue is expected to cover the latest achievements in the field of artificial intelligence technology and the integrated development of marine energy. Research topics include but are not limited to the application of AI in the design of new marine energy systems, optimization, and operation, as well as the AI-driven integration of offshore energy and marine resource industries.

History: The random fluctuations in renewable energy and the complex marine environments have limited the use of Marine Renewable Energy in the open sea. The rapid development of artificial intelligence (AI) technology covers many aspects of data analysis, predictive models, and more.

Cutting-Edge Research: application of AI in the design of new marine energy systems, optimization, and operation; AI-driven integration of offshore energy and marine resource industries.

Types of Articles Needed: reviews or research papers in related fields.

Prof. Dr. Zhaolong Han
Prof. Dr. Jianhua Zhang
Dr. Zhongqiang Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI
  • artificial intelligence
  • ocean renewable energy
  • offshore wind power
  • energy system optimization
  • integration of marine energy and resources
  • comprehensive utilization

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Published Papers (3 papers)

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Research

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25 pages, 8237 KiB  
Article
A Machine Learning Approach for the Clustering and Classification of Geothermal Reservoirs in the Ying-Qiong Basin
by Yujing Duan, Yuan Liang, Qingyun Ji and Zhong Wang
J. Mar. Sci. Eng. 2025, 13(3), 415; https://doi.org/10.3390/jmse13030415 - 23 Feb 2025
Viewed by 518
Abstract
The exploration and development of marine geothermal energy is a field with significant potential, but it is also one that presents considerable challenges and costs. The assessment of marine geothermal reservoir potential is currently based on subjective analysis, and this study proposes an [...] Read more.
The exploration and development of marine geothermal energy is a field with significant potential, but it is also one that presents considerable challenges and costs. The assessment of marine geothermal reservoir potential is currently based on subjective analysis, and this study proposes an innovative clustering-based method to classify marine geothermal reservoirs systematically. The Yingqiong Basin was analysed to develop a machine learning framework to predict the potential of marine geothermal reservoirs (CPPOGR). The study integrated eight key geothermal features into a unified dataset, employing dimensionality reduction techniques (principal component analysis and sparse autoencoder) and SMOTE to balance the sample size. Machine learning classifiers, including XGBoost, BP Neural Networks, Support Vector Machines, K-Nearest Neighbours, and Random Forests, were utilised for prediction. The experimental results demonstrate that XGBoost is the most suitable classifier, achieving an excellent performance of 0.96 precision, 0.9556 recall, 0.9528 F1 score, and 0.9623 accuracy. These results demonstrate the effectiveness of the proposed CPPOGR in accurately classifying marine geothermal reservoirs based on intrinsic features. This study underscores the potential of integrating cluster analysis with machine learning for efficient reservoir characterisation, thereby offering a novel approach for marine geothermal resource assessment. Full article
(This article belongs to the Special Issue AI-Empowered Marine Energy)
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18 pages, 2663 KiB  
Article
Improved RT-DETR for Infrared Ship Detection Based on Multi-Attention and Feature Fusion
by Chun Liu, Yuanliang Zhang, Jingfu Shen and Feiyue Liu
J. Mar. Sci. Eng. 2024, 12(12), 2130; https://doi.org/10.3390/jmse12122130 - 22 Nov 2024
Cited by 2 | Viewed by 2359
Abstract
Infrared cameras form images by capturing the thermal radiation emitted by objects in the infrared spectrum, making them complex sensors widely used in maritime surveillance. However, the broad spectral range of the infrared band makes it susceptible to environmental interference, which can reduce [...] Read more.
Infrared cameras form images by capturing the thermal radiation emitted by objects in the infrared spectrum, making them complex sensors widely used in maritime surveillance. However, the broad spectral range of the infrared band makes it susceptible to environmental interference, which can reduce the contrast between the target and the background. As a result, detecting infrared targets in complex marine environments remains challenging. This paper presents a novel and enhanced detection model developed from the real-time detection transformer (RT-DETR), which is designated as MAFF-DETR. The model incorporates a novel backbone by integrating CSP and parallelized patch-aware attention to enhance sensitivity to infrared imagery. Additionally, a channel attention module is employed during feature selection, leveraging high-level features to filter low-level information and enabling efficient multi-level fusion. The model’s target detection performance on resource-constrained devices is further enhanced by incorporating advanced techniques such as group convolution and ShuffleNetV2. The experimental results show that, although the enhanced RT-DETR algorithm still experiences missed detections under severe object occlusion, it has significantly improved overall performance, including a 1.7% increase in mAP, a reduction in 4.3 M parameters, and a 5.8 GFLOPs decrease in computational complexity. It can be widely applied to tasks such as coastline monitoring and maritime search and rescue. Full article
(This article belongs to the Special Issue AI-Empowered Marine Energy)
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Review

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29 pages, 2777 KiB  
Review
Digitalization in the Maritime Logistics Industry: A Systematic Literature Review of Enablers and Barriers
by Fangli Zeng, Anqi Chen, Shuojiang Xu, Hing Kai Chan and Yusong Li
J. Mar. Sci. Eng. 2025, 13(4), 797; https://doi.org/10.3390/jmse13040797 - 16 Apr 2025
Viewed by 1246
Abstract
Digitalization is gaining its popularity in the maritime logistics sector due to its potential to enhance information sharing and automation. These advantages can significantly improve efficiency and have the potential to replace complex manual tasks. However, the diffusion of digitalization faces certain challenges, [...] Read more.
Digitalization is gaining its popularity in the maritime logistics sector due to its potential to enhance information sharing and automation. These advantages can significantly improve efficiency and have the potential to replace complex manual tasks. However, the diffusion of digitalization faces certain challenges, which, in turn, has drawn the attention of researchers. Implementing digitalization is a complex process, as it is affected by various enablers and barriers, while research providing a comprehensive overview of digitalization in the maritime logistics sector is limited. This study aims to fill the gap by conducting a literature review that reveals digitalization’s enablers and barriers in the maritime logistics sector and constructs a theoretical framework. It analyzes 117 articles that have made significant contributions to this field. The development of innovative technologies, such as blockchain, digital twins, and autonomous shipping, fosters digitalization in maritime logistics. Conversely, barriers like the lack of awareness about the benefits of digitalization can slow down its progress. In total, this paper identifies 19 enablers of and 10 barriers to digitalization in the maritime logistics sector. These enablers and barriers are classified into three groups–technology, organization, and environment–following the Technology–Organization–Environment (TOE) framework. We develop a theoretical framework accordingly using, as its basis, relevant innovation diffusion theories and studies. This study contributes to the development of effective digitalization strategies for maritime organizations and provides a theoretical foundation for future research. Full article
(This article belongs to the Special Issue AI-Empowered Marine Energy)
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