IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v320y2025ics0360544225009296.html
   My bibliography  Save this article

Study on the effect of wind direction on the thermal performance of cavity receiver for trough solar system: Artificial neural network approach based on genetic algorithm

Author

Listed:
  • Wang, Zhimin
  • Kong, Fance
  • Chan, Wenwu
  • Yue, Shangyu

Abstract

The structure of the receiver will affect the thermal performance of the trough solar system. Based on the typical characteristics of alpine areas, it is beneficial to analyze the effects of different wind directions and temperatures on the thermal performance of the developed new inverted trapezoidal cavity receiver, to improve the efficiency. In this paper, a comprehensive approach is proposed to establish an indoor controllable experimental platform. Backpropagation (BP) and Genetic Algorithm-Backpropagation (GA-BP) artificial neural networks and Support Vector Regression (SVR) are established, to predict and validate the variation of the heat loss of the inverted trapezoidal cavity receiver. The results show that the heat transfer in the cavity is least perturbed at −45° wind direction. There is a critical value of heat loss in the cavity between 343 K and 363 K, and the thermal performance is moderately improved above the critical temperature. Genetic Algorithm-Backpropagation (GA-BP) performs best in predicting the thermal performance accuracy of the cavity receiver. The four evaluation indexes are 0.021, 0.011, 1.4 %, and 0.002, respectively, which are the lowest, which verifies the effectiveness of the artificial neural network in accurately predicting the thermal performance of the cavity receiver under complex nonlinear conditions.

Suggested Citation

  • Wang, Zhimin & Kong, Fance & Chan, Wenwu & Yue, Shangyu, 2025. "Study on the effect of wind direction on the thermal performance of cavity receiver for trough solar system: Artificial neural network approach based on genetic algorithm," Energy, Elsevier, vol. 320(C).
    • Handle: RePEc:eee:energy:v:320:y:2025:i:c:s0360544225009296
    DOI: 10.1016/j.energy.2025.135287
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225009296
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.135287?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Shuaishuai & Yang, Bin & Zhi, Yuan & Yu, Xiaohui, 2023. "Thermal-mechanical performance analysis of parabolic trough receivers under various optical errors based on coupled optical-thermal-stress model," Renewable Energy, Elsevier, vol. 210(C), pages 687-700.
    2. Herez, Amal & El Hage, Hicham & Lemenand, Thierry & Ramadan, Mohamad & Khaled, Mahmoud, 2021. "Parabolic trough photovoltaic/thermal hybrid system: Thermal modeling and parametric analysis," Renewable Energy, Elsevier, vol. 165(P1), pages 224-236.
    3. Moosavian, Seyed Farhan & Borzuei, Daryoosh & Ahmadi, Abolfazl, 2021. "Energy, exergy, environmental and economic analysis of the parabolic solar collector with life cycle assessment for different climate conditions," Renewable Energy, Elsevier, vol. 165(P1), pages 301-320.
    4. Sun, Fei & Jin, Tongdan, 2022. "A hybrid approach to multi-step, short-term wind speed forecasting using correlated features," Renewable Energy, Elsevier, vol. 186(C), pages 742-754.
    5. Rajan, Abhinav & Reddy, K.S., 2023. "Integrated optical and thermal model to investigate the performance of a solar parabolic dish collector coupled with a cavity receiver," Renewable Energy, Elsevier, vol. 219(P1).
    6. Abdulhamed, Ali Jaber & Adam, Nor Mariah & Ab-Kadir, Mohd Zainal Abidin & Hairuddin, Abdul Aziz, 2018. "Review of solar parabolic-trough collector geometrical and thermal analyses, performance, and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 822-831.
    7. Zhang, Yagang & Pan, Guifang & Chen, Bing & Han, Jingyi & Zhao, Yuan & Zhang, Chenhong, 2020. "Short-term wind speed prediction model based on GA-ANN improved by VMD," Renewable Energy, Elsevier, vol. 156(C), pages 1373-1388.
    8. Pirmoradi, Neda & Ghaneian, Mohammad Taghi & Ehrampoush, Mohammad Hassan & Salmani, Mohammad Hossein & Hatami, Behnam, 2021. "The conversion of poultry slaughterhouse wastewater sludge into biodiesel: Process modeling and optimization," Renewable Energy, Elsevier, vol. 178(C), pages 1236-1249.
    9. Wasankar, Kushal S. & Gulhane, Nitin P. & Taler, Jan & Taler, Dawid & Ocłoń, Paweł & Vallati, Andrea, 2024. "Numerical and experimental analysis on convective heat losses from a fully open cylindrical cascaded cavity receiver," Energy, Elsevier, vol. 288(C).
    10. Bosbach, Johannes & Pennecot, Julien & Wagner, Claus & Raffel, Markus & Lerche, Thomas & Repp, Stefan, 2006. "Experimental and numerical simulations of turbulent ventilation in aircraft cabins," Energy, Elsevier, vol. 31(5), pages 694-705.
    11. Karimi, Reza & Gheinani, Touraj Tavakoli & Madadi Avargani, Vahid, 2018. "A detailed mathematical model for thermal performance analysis of a cylindrical cavity receiver in a solar parabolic dish collector system," Renewable Energy, Elsevier, vol. 125(C), pages 768-782.
    12. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    13. Fuqiang, Wang & Ziming, Cheng & Jianyu, Tan & Yuan, Yuan & Yong, Shuai & Linhua, Liu, 2017. "Progress in concentrated solar power technology with parabolic trough collector system: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1314-1328.
    14. Wang, Shouxiang & Zhang, Na & Wu, Lei & Wang, Yamin, 2016. "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method," Renewable Energy, Elsevier, vol. 94(C), pages 629-636.
    15. Lillo-Bravo, I. & Vera-Medina, J. & Fernandez-Peruchena, C. & Perez-Aparicio, E. & Lopez-Alvarez, J.A. & Delgado-Sanchez, J.M., 2023. "Random Forest model to predict solar water heating system performance," Renewable Energy, Elsevier, vol. 216(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    2. Da Liu & Kun Sun & Han Huang & Pingzhou Tang, 2018. "Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory," Sustainability, MDPI, vol. 10(9), pages 1-22, September.
    3. Ajbar, Wassila & Parrales, A. & Huicochea, A. & Hernández, J.A., 2022. "Different ways to improve parabolic trough solar collectors’ performance over the last four decades and their applications: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    4. Li, Ke & Shen, Ruifang & Wang, Zhenguo & Yan, Bowen & Yang, Qingshan & Zhou, Xuhong, 2023. "An efficient wind speed prediction method based on a deep neural network without future information leakage," Energy, Elsevier, vol. 267(C).
    5. Bai, Yulong & Liu, Ming-De & Ding, Lin & Ma, Yong-Jie, 2021. "Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition," Applied Energy, Elsevier, vol. 301(C).
    6. Rajan, Abhinav & Reddy, K.S., 2025. "Integrated optical-thermal model to predict the performance of a solar parabolic dish collector for process heating applications," Energy, Elsevier, vol. 321(C).
    7. Yang, Yusong & Solomin, Evgeny V., 2025. "Wind direction prediction based on nonlinear autoregression and Elman neural networks for the wind turbine yaw system," Renewable Energy, Elsevier, vol. 241(C).
    8. Abubakr, Mohamed & Amein, Hamza & Akoush, Bassem M. & El-Bakry, M. Medhat & Hassan, Muhammed A., 2020. "An intuitive framework for optimizing energetic and exergetic performances of parabolic trough solar collectors operating with nanofluids," Renewable Energy, Elsevier, vol. 157(C), pages 130-149.
    9. Zhou, Yilin & Wang, Jianzhou & Lu, Haiyan & Zhao, Weigang, 2022. "Short-term wind power prediction optimized by multi-objective dragonfly algorithm based on variational mode decomposition," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    10. Yang, S. & Ordonez, J.C., 2019. "3D thermal-hydraulic analysis of a symmetric wavy parabolic trough absorber pipe," Energy, Elsevier, vol. 189(C).
    11. Marco Milanese & Gianpiero Colangelo & Arturo de Risi, 2021. "Development of a High-Flux Solar Simulator for Experimental Testing of High-Temperature Applications," Energies, MDPI, vol. 14(11), pages 1-18, May.
    12. Hachicha, Ahmed Amine & Yousef, Bashria A.A. & Said, Zafar & Rodríguez, Ivette, 2019. "A review study on the modeling of high-temperature solar thermal collector systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 280-298.
    13. Madadi Avargani, Vahid & Norton, Brian & Rahimi, Amir, 2021. "An open-aperture partially-evacuated receiver for more uniform reflected solar flux in circular-trough reflectors: Comparative performance in air heating applications," Renewable Energy, Elsevier, vol. 176(C), pages 11-24.
    14. Liu, Shuaishuai & Yang, Bin & Hou, Yutian & Yu, Xiaohui, 2022. "Effects of geometric configurations on the thermal-mechanical properties of parabolic trough receivers based on coupled optical-thermal-stress model," Renewable Energy, Elsevier, vol. 199(C), pages 929-942.
    15. Leng, Zhiyuan & Chen, Lu & Yi, Bin & Liu, Fanqian & Xie, Tao & Mei, Ziyi, 2025. "Short-term wind speed forecasting based on a novel KANInformer model and improved dual decomposition," Energy, Elsevier, vol. 322(C).
    16. Hassan, Atazaz & Quanfang, Chen & Abbas, Sajid & Lu, Wu & Youming, Luo, 2021. "An experimental investigation on thermal and optical analysis of cylindrical and conical cavity copper tube receivers design for solar dish concentrator," Renewable Energy, Elsevier, vol. 179(C), pages 1849-1864.
    17. Gharat, Punit V. & Bhalekar, Snehal S. & Dalvi, Vishwanath H. & Panse, Sudhir V. & Deshmukh, Suresh P. & Joshi, Jyeshtharaj B., 2021. "Chronological development of innovations in reflector systems of parabolic trough solar collector (PTC) - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    18. Ma, Haiwen & Liu, Peng & Huang, Lu & Ren, Tingting & Ge, Yanlin & Chen, Lingen, 2024. "Heat transfer enhancement in parabolic trough receiver based on exergy destruction minimization," Energy, Elsevier, vol. 313(C).
    19. Forman, Patrick & Penkert, Sebastian & Kämper, Christoph & Stallmann, Tobias & Mark, Peter & Schnell, Jürgen, 2020. "A survey of solar concrete shell collectors for parabolic troughs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    20. Rahimi Telwar, Donya & Khodaei, Jalal & Samimi-Akhijahani, Hadi, 2024. "Thermo-economic evaluation and structural simulation of a parabolic solar collector (PTC) integrated with a desalination system," Energy, Elsevier, vol. 299(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:320:y:2025:i:c:s0360544225009296. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.