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

Author

Listed:
  • Jafar Tavoosi

    (Department of Electrical Engineering, Faculty of Engineering, Ilam University, Ilam, Iran)

  • Amir Abolfazl Suratgar

    (Center of Excellence on Control and Robotics, Department of Electrical Engineering, Amirkabir University of Technology, 424 Hafez Ave, Tehran, Iran)

  • Mohammad Bagher Menhaj

    (Center of Excellence on Control and Robotics, Department of Electrical Engineering, Amirkabir University of Technology, 424 Hafez Ave, Tehran, Iran)

  • Amir Mosavi

    (Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
    John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary)

  • Ardashir Mohammadzadeh

    (Department of Electrical Engineering, University of Bonab, Bonab 551761167, Iran)

  • Ehsan Ranjbar

    (Center of Excellence on Control and Robotics, Department of Electrical Engineering, Amirkabir University of Technology, 424 Hafez Ave, Tehran, Iran)

Abstract

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.

Suggested Citation

  • Jafar Tavoosi & Amir Abolfazl Suratgar & Mohammad Bagher Menhaj & Amir Mosavi & Ardashir Mohammadzadeh & Ehsan Ranjbar, 2021. "Modeling Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System for Power Prediction," Sustainability, MDPI, vol. 13(6), pages 1-21, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:6:p:3301-:d:518780
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    References listed on IDEAS

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    1. Osório, G.J. & Matias, J.C.O. & Catalão, J.P.S., 2015. "Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information," Renewable Energy, Elsevier, vol. 75(C), pages 301-307.
    2. Grahovac, Jovana & Jokić, Aleksandar & Dodić, Jelena & Vučurović, Damjan & Dodić, Siniša, 2016. "Modelling and prediction of bioethanol production from intermediates and byproduct of sugar beet processing using neural networks," Renewable Energy, Elsevier, vol. 85(C), pages 953-958.
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