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

• Type: Another Publication in an International Scientific Journal
• Date: 2023

Authors

Clemente, D (Author) Other; Teixeira-Duarte, F (Author) Other; Taveira-Pinto, F (Author) Other

Journal

Title: Energies

Vol. 16 No. 4

Final page: 4660

ISSN: 1996-1073

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00Y-PXD

DOI: 10.3390/en16124660

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 28