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Article

Modeling Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System for Power Prediction

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Department of Electrical Engineering, Faculty of Engineering, Ilam University, Ilam, Iran
2
Center of Excellence on Control and Robotics, Department of Electrical Engineering, Amirkabir University of Technology, 424 Hafez Ave, Tehran, Iran
3
Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
4
John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
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Department of Electrical Engineering, University of Bonab, Bonab 551761167, Iran
*
Authors to whom correspondence should be addressed.
Academic Editor: Mehdi Seyedmahmoudian
Sustainability 2021, 13(6), 3301; https://doi.org/10.3390/su13063301
Received: 1 February 2021 / Revised: 5 March 2021 / Accepted: 7 March 2021 / Published: 17 March 2021
A novel Nonlinear Consequent Part Recurrent Type-2 Fuzzy System (NCPRT2FS) is presented for the modeling of renewable energy systems. Not only does this paper present a new architecture of the type-2 fuzzy system (T2FS) for identification and behavior prognostication of an experimental solar cell set and a wind turbine, but also, it introduces an exquisite technique to acquire an optimal number of membership functions (MFs) and their corresponding rules. Using nonlinear functions in the “Then” part of fuzzy rules, introducing a new mechanism in structure learning, using an adaptive learning rate and performing convergence analysis of the learning algorithm are the innovations of this paper. Another novel innovation is using optimization techniques (including pruning fuzzy rules, initial adjustment of MFs). Next, a solar photovoltaic cell and a wind turbine are deemed as case studies. The experimental data are exploited and the consequent yields emerge as convincing. The root-mean-square-error (RMSE) is less than 0.006 and the number of fuzzy rules is equal to or less than four rules, which indicates the very good performance of the presented fuzzy neural network. Finally, the obtained model is used for the first time for a geographical area to examine the feasibility of renewable energies. View Full-Text
Keywords: self-evolving; nonlinear consequent part; convergence analysis; renewable energy; type-2 fuzzy; artificial intelligence; machine learning; big data; data science; fuzzy logic; energy self-evolving; nonlinear consequent part; convergence analysis; renewable energy; type-2 fuzzy; artificial intelligence; machine learning; big data; data science; fuzzy logic; energy
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MDPI and ACS Style

Tavoosi, J.; Suratgar, A.A.; Menhaj, M.B.; Mosavi, A.; Mohammadzadeh, A.; Ranjbar, E. Modeling Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System for Power Prediction. Sustainability 2021, 13, 3301. https://doi.org/10.3390/su13063301

AMA Style

Tavoosi J, Suratgar AA, Menhaj MB, Mosavi A, Mohammadzadeh A, Ranjbar E. Modeling Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System for Power Prediction. Sustainability. 2021; 13(6):3301. https://doi.org/10.3390/su13063301

Chicago/Turabian Style

Tavoosi, Jafar, Amir A. Suratgar, Mohammad B. Menhaj, Amir Mosavi, Ardashir Mohammadzadeh, and Ehsan Ranjbar. 2021. "Modeling Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System for Power Prediction" Sustainability 13, no. 6: 3301. https://doi.org/10.3390/su13063301

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