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Light environment simulation for a three-span plastic greenhouse based on greenhouse light environment simulation software

Author

Listed:
  • Bo, Yu
  • Zhang, Yu
  • Zheng, Kunpeng
  • Zhang, Jingxu
  • Wang, Xiaochan
  • Sun, Jin
  • Wang, Jian
  • Shu, Sheng
  • Wang, Yu
  • Guo, Shirong

Abstract

Light environment research in greenhouses is mainly focused on solar greenhouses with simple structures; the universality of illumination models is low to date. To study the light environments in various of greenhouses, Greenhouse Light Environment Simulation software has been developed based on the ray tracing and Monte Carlo methods. The function of the software is to analyse the light path of the sampling point. The software has been verified and applied in a three-span plastic greenhouse. The results show that the mean relative errors of each month and typical weather conditions are less than 10%. The mean relative error of the simulated mean at any point in the greenhouse is less than 4%. The solar radiation intensity in the greenhouse is mainly affected by the solar incidence angle, and the solar radiation uniformity is mainly affected by the roof shape; the average solar radiation intensity in the greenhouse is the highest in August (over 250 W/m2), and the lowest in December, (below 60 W/m2). When the greenhouse is oriented at 10° east by south, the total solar radiation is 2% higher and the uniformity of daily solar radiation is 0.8% higher than when the greenhouse is oriented to the south.

Suggested Citation

  • Bo, Yu & Zhang, Yu & Zheng, Kunpeng & Zhang, Jingxu & Wang, Xiaochan & Sun, Jin & Wang, Jian & Shu, Sheng & Wang, Yu & Guo, Shirong, 2023. "Light environment simulation for a three-span plastic greenhouse based on greenhouse light environment simulation software," Energy, Elsevier, vol. 271(C).
    • Handle: RePEc:eee:energy:v:271:y:2023:i:c:s0360544223003602
    DOI: 10.1016/j.energy.2023.126966
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    References listed on IDEAS

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    1. Petržala, J. & Kómar, L. & Kocifaj, M., 2017. "An advanced clear-sky model for more accurate irradiance and illuminance predictions for arbitrarily oriented inclined surfaces," Renewable Energy, Elsevier, vol. 106(C), pages 212-221.
    2. Iddio, E. & Wang, L. & Thomas, Y. & McMorrow, G. & Denzer, A., 2020. "Energy efficient operation and modeling for greenhouses: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    3. Cao, Fei & Li, Huashan & Yang, Tian & Li, Yan & Zhu, Tianyu & Zhao, Liang, 2017. "Evaluation of diffuse solar radiation models in Northern China: New model establishment and radiation sources comparison," Renewable Energy, Elsevier, vol. 103(C), pages 708-720.
    4. Singh, Gurpreet & Singh, Parm Pal & Lubana, Prit Pal Singh & Singh, K.G., 2006. "Formulation and validation of a mathematical model of the microclimate of a greenhouse," Renewable Energy, Elsevier, vol. 31(10), pages 1541-1560.
    5. Xiaodan Zhang & Jian Lv & Jianming Xie & Jihua Yu & Jing Zhang & Chaonan Tang & Jing Li & Zhixue He & Cheng Wang, 2020. "Solar Radiation Allocation and Spatial Distribution in Chinese Solar Greenhouses: Model Development and Application," Energies, MDPI, vol. 13(5), pages 1-27, March.
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