Advancements on Optimization Algorithms Applied to Wave Energy Assessment: An Overview on Wave Climate and Energy Resource

The wave energy sector has not reached a sufficient level of maturity for commercial competitiveness, thus requiring further efforts towards optimizing existing technologies and making wave energy a viable alternative to bolster energy mixes. Usually, these efforts are supported by physical and nume...

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Bibliographic Details
Published in:Energies (Basel) 2023-06, Vol.16 (12), p.4660
Main Authors: Clemente, Daniel, Teixeira-Duarte, Felipe, Rosa-Santos, Paulo, Taveira-Pinto, Francisco
Format: Article
Language:English
Subjects:
Algorithms
Alternative energy
Alternative energy sources
Analysis
Artificial intelligence
Case studies
Competitiveness
Computer applications
Electricity generation
Energy industry
Energy sources
evolutionary algorithms
Flow velocity
Genetic algorithms
Learning curves
Mathematical optimization
metaheuristic algorithms
Neural networks
Neurons
Numerical models
Optimization
Optimization algorithms
renewable wave energy
Reviews
Software
Solution space
Survivability
wave conditions prediction
Wave energy
Wave power
Wave propagation
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Summary:The wave energy sector has not reached a sufficient level of maturity for commercial competitiveness, thus requiring further efforts towards optimizing existing technologies and making wave energy a viable alternative to bolster energy mixes. Usually, these efforts are supported by physical and numerical modelling of complex physical phenomena, which require extensive resources and time to obtain reliable, yet limited results. To complement these approaches, artificial-intelligence-based techniques (AI) are gaining increasing interest, given their computational speed and capability of searching large solution spaces and/or identifying key study patterns. Under this scope, this paper presents a comprehensive review on the use of computational systems and AI-based techniques to wave climate and energy resource studies. The paper reviews different optimization methods, analyses their application to extreme events and examines their use in wave propagation and forecasting, which are pivotal towards ensuring survivability and assessing the local wave operational conditions, respectively. The use of AI has shown promising results in improving the efficiency, accuracy and reliability of wave predictions and can enable a more thorough and automated sweep of alternative design solutions, within a more reasonable timeframe and at a lower computational cost. However, the particularities of each case study still limit generalizations, although some application patterns have been identified—such as the frequent use of neural networks.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16124660