Advances in Wave Energy Conversion with Data-Driven Models
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: 5 June 2024 | Viewed by 1087
Special Issue Editors
Interests: marine renewable energies; coastal and ocean engineering; composite modelling applied to wave energy conversion; wave-structure interactions; offshore aquaculture; artificial intelligence
Interests: marine renewable energies; coastal and port engineering; physical and numerical modelling applied to offshore, port, and coastal issues; wave energy harvesting
Special Issues, Collections and Topics in MDPI journals
2. CIIMAR—Interdisciplinary Centre of Marine and Environmental Research, Marine Energy and Hydraulic Structures, 4450-208 Matosinhos, Portugal
Interests: coastal defense; coastal engineering; coastal structures; breakwaters; marine energy; integrated coastal zone management; nature-based solutions
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The advent of advanced data-driven models, including artificial intelligence, can greatly benefit the development of wave energy converters (WECs), a promising field of research capable of expanding the available renewable energy mix. Data-driven models employ large datasets from resource-demanding physics-driven models to predict, classify, and assist in decision-making and optimization processes at a much lower cost and timeframe, so long as they are adequately trained.
This Special Issue seeks to bring together cutting-edge research that utilizes data-driven approaches to optimize the design, performance, control, and operational aspects of WECs and their sub-components, from mooring systems to the power take-off. This Special Issue encompasses various aspects, including stochastic techniques, meta-heuristic methods, machine learning algorithms, optimization methods, and case studies showcasing successful applications. We invite high-quality research papers that contribute to the advancement of WEC development and highlight the potential of data-driven models in promoting WEC efficiency, reliability, and commercial viability.
Dr. Daniel Clemente
Dr. Paulo Rosa Santos
Prof. Dr. Francisco Taveira Pinto
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
- marine renewable energy
- wave energy converter optimization
- artificial intelligence
- machine learning
- deep learning
- statistical techniques
- system identification
- neural networks
- evolutionary algorithms
- fuzzy-logic
- digital twins
- data assimilation
- performance forecasts
