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Title: Evaluation of the accuracy of soft computing learning algorithms in performance prediction of tidal turbine
Authors: Band, SS
Ghazvinei, PT
Yusof, KB
Ahmadi, MH
Nabipour, N
Chau, KW 
Issue Date: May-2021
Source: Energy science & engineering, May 2021, v. 9, no. 5, p. 633-644
Abstract: Marine renewable energy has made significant progress in the last few decades. Even after making substantial progress, the cost of electricity produced by tidal turbines is high. Therefore, the current paper concentrated on reducing the cost of transportation and installation of the turbine by performing a model. Extreme Learning Machine and Support Vector Machines as well as Genetic Programming were applied to predict the performance of the turbine model by creating short-term, multistep-ahead prediction models to compute the performance of the H-rotor vertical axis Folding Tidal turbine. The performance of the turbine was verified by a numerical study using the three-dimensional approach for the viscous model with the unsteady flow. Statistical evaluation of the outcomes pointed out that advanced Extreme Learning Machine simulation made the assurance in formulating an innovative forecasting strategy for investigating the performances of the tidal turbine. This study shows that the application of the new procedure resulted in confident generality performance and learns faster than orthodox learning algorithms. In conclusion, the assessment indicated that the advanced Extreme Learning Machine simulation was capable as a promising alternative to existing numerical methods for computing the coefficient of performance for turbines.
Keywords: Co-efficient of performance
Extreme learning machine
Folding tidal turbine
Genetic programming
Support vector machines
Tidal current turbine
Publisher: John Wiley & Sons Ltd.
Journal: Energy science & engineering 
EISSN: 2050-0505
DOI: 10.1002/ese3.849
Rights: © 2020 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution License (, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
The following publication Band SS, Taherei Ghazvinei P, bin Wan Yusof K, Hossein Ahmadi M, Nabipour N, Chau K-W. Evaluation of the accuracy of soft computing learning algorithms in performance prediction of tidal turbine. Energy Sci Eng. 2021;9:633–644 is available at
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