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

Daniel Clemente (), Felipe Teixeira-Duarte, Paulo Rosa-Santos () and Francisco Taveira-Pinto
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Daniel Clemente: CIIMAR—Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208 Matosinhos, Portugal
Felipe Teixeira-Duarte: CIIMAR—Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208 Matosinhos, Portugal
Paulo Rosa-Santos: CIIMAR—Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208 Matosinhos, Portugal
Francisco Taveira-Pinto: CIIMAR—Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208 Matosinhos, Portugal

Energies, 2023, vol. 16, issue 12, 1-28

Abstract: 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.

Keywords: renewable wave energy; artificial intelligence; metaheuristic algorithms; neural networks; evolutionary algorithms; wave conditions prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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