TY - JOUR
T1 - 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
AU - Altunkaynak, Abdüsselam
AU - Çelik, Anıl
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/8
Y1 - 2022/8
N2 - 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.
AB - 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.
KW - Fuzzy logic
KW - Genetic algorithms
KW - Hydrodynamic efficiency
KW - Oscillating water column
KW - Physical experimental model
KW - Wave energy
UR - http://www.scopus.com/inward/record.url?scp=85133725485&partnerID=8YFLogxK
U2 - 10.1016/j.renene.2022.06.045
DO - 10.1016/j.renene.2022.06.045
M3 - Article
AN - SCOPUS:85133725485
SN - 0960-1481
VL - 196
SP - 99
EP - 110
JO - Renewable Energy
JF - Renewable Energy
ER -