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: closed (5 June 2024) | Viewed by 1880
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
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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.
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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
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