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Adaptive Neuro-Fuzzy Inference System modelling for performance prediction of solar thermal energy system

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  • Yaïci, Wahiba
  • Entchev, Evgueniy

Abstract

This study investigates in details the applicability of Adaptive Neuro-Fuzzy Inference System (ANFIS) approach for predicting the performance parameters of a solar thermal energy system. Experiments were conducted on the system under a broad range of operating conditions during different Canadian seasons and weather conditions. The experimental data were used for developing the ANFIS network model. This later was then optimised and applied to predict various performance parameters of the system.

Suggested Citation

  • Yaïci, Wahiba & Entchev, Evgueniy, 2016. "Adaptive Neuro-Fuzzy Inference System modelling for performance prediction of solar thermal energy system," Renewable Energy, Elsevier, vol. 86(C), pages 302-315.
    • Handle: RePEc:eee:renene:v:86:y:2016:i:c:p:302-315
    DOI: 10.1016/j.renene.2015.08.028
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    6. Kaloop, Mosbeh R. & Bardhan, Abidhan & Kardani, Navid & Samui, Pijush & Hu, Jong Wan & Ramzy, Ahmed, 2021. "Novel application of adaptive swarm intelligence techniques coupled with adaptive network-based fuzzy inference system in predicting photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    7. Kotur, Dimitrije & Đurišić, Željko, 2017. "Optimal spatial and temporal demand side management in a power system comprising renewable energy sources," Renewable Energy, Elsevier, vol. 108(C), pages 533-547.
    8. Wahiba Yaïci & Michela Longo & Evgueniy Entchev & Federica Foiadelli, 2017. "Simulation Study on the Effect of Reduced Inputs of Artificial Neural Networks on the Predictive Performance of the Solar Energy System," Sustainability, MDPI, vol. 9(8), pages 1-14, August.
    9. Rana, Mashud & Sethuvenkatraman, Subbu & Heidari, Rahmat & Hands, Stuart, 2022. "Solar thermal generation forecast via deep learning and application to buildings cooling system control," Renewable Energy, Elsevier, vol. 196(C), pages 694-706.
    10. Halabi, Laith M. & Mekhilef, Saad & Hossain, Monowar, 2018. "Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation," Applied Energy, Elsevier, vol. 213(C), pages 247-261.
    11. A. Bassam & O. May Tzuc & M. Escalante Soberanis & L. J. Ricalde & B. Cruz, 2017. "Temperature Estimation for Photovoltaic Array Using an Adaptive Neuro Fuzzy Inference System," Sustainability, MDPI, vol. 9(8), pages 1-16, August.
    12. Heng, Shye Yunn & Asako, Yutaka & Suwa, Tohru & Nagasaka, Ken, 2019. "Transient thermal prediction methodology for parabolic trough solar collector tube using artificial neural network," Renewable Energy, Elsevier, vol. 131(C), pages 168-179.
    13. Unterberger, Viktor & Lichtenegger, Klaus & Kaisermayer, Valentin & Gölles, Markus & Horn, Martin, 2021. "An adaptive short-term forecasting method for the energy yield of flat-plate solar collector systems," Applied Energy, Elsevier, vol. 293(C).
    14. Chao-Rong Chen & Faouzi Brice Ouedraogo & Yu-Ming Chang & Devita Ayu Larasati & Shih-Wei Tan, 2021. "Hour-Ahead Photovoltaic Output Forecasting Using Wavelet-ANFIS," Mathematics, MDPI, vol. 9(19), pages 1-14, October.

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