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Evaluation of the accuracy of soft computing learning algorithms in performance prediction of tidal turbine

Band, S.S. and Taherei Ghazvinei, P. and bin Wan Yusof, K. and Hossein Ahmadi, M. and Nabipour, N. and Chau, K.-W. (2021) Evaluation of the accuracy of soft computing learning algorithms in performance prediction of tidal turbine. Energy Science and Engineering, 9 (5). pp. 633-644.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

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. © 2020 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.

Item Type:Article
Impact Factor:cited By 0
Uncontrolled Keywords:Forecasting; Genetic algorithms; Genetic programming; Knowledge acquisition; Learning systems; Numerical methods; Predictive analytics; Soft computing; Support vector machines; Turbines, Cost of electricity; Cost of transportation; Extreme learning machine; Marine renewable energy; Multi-step-ahead predictions; Performance prediction; Statistical evaluation; Turbine modeling, Learning algorithms
ID Code:23942
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:19 Aug 2021 13:23
Last Modified:19 Aug 2021 13:23

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