A novel Geno-fuzzy based model for hydrodynamic efficiency prediction of a land-fixed oscillating water column for various front wall openings, power take-off dampings and incident wave steepnesses

Abdüsselam Altunkaynak and Anıl Çelik

Renewable Energy, 2022, vol. 196, issue C, 99-110

Abstract: Accurate efficiency estimation of a wave energy converter (WEC) is a key concept in the design stage. Oscillating water column (OWC) is a promising type of WEC due to its advantages such as proved concept, operational simplicity, accessibility, reliability etc. In this study, for accurate efficiency estimation of an OWC, a novel Geno-fuzzy based model (GENOFIS) was developed, firstly, by improving Adaptive Neuro-Fuzzy inference system (ANFIS) and secondly, incorporating the Genetic algorithms (GAs). Data for training and testing phases of the models were obtained from an extensive wave flume experiments for various OWC underwater opening heights and applied PTO dampings under different incident waves. Both models performed remarkably with a slightly better performance of GENOFIS. The superiority of the GENOFIS model stemmed from that its high performance was attained with substantially low fuzzy rules (only three) where ANFIS required incomparably large fuzzy rules (twenty-seven) and yet achieved a slightly lower performance. Accordingly, very few numbers of fuzzy rules enable the construction of GENOFIS model structure with low complexity, which, in turn, immensely reduce the computational time required. Developed less complicated GENOFIS model is parsimonious, unlikely to suffer from overfitting and has high interpretability and practicality.

Keywords: Fuzzy logic; Hydrodynamic efficiency; Genetic algorithms; Oscillating water column; Wave energy; Physical experimental model (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:196:y:2022:i:c:p:99-110

DOI: 10.1016/j.renene.2022.06.045

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