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Generation of typical meteorological year for integrated climate based daylight modeling and building energy simulation

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  • Sun, Jingting
  • Li, Zhengrong
  • Xiao, Fu
  • Xiao, Jianzhuang

Abstract

Daylight plays an indispensable role in promoting the energy efficiency of buildings and satisfying the health and productivity requirements of occupants. Dynamic daylight and thermal modeling approaches have been widely adopted to evaluate the energy performance of daylight-utilized design. At present there has been no typical meteorological year (TMY) files specifically developed for daylight-utilized building energy simulation. In this study, a feasible TMY generation method is developed specifically for integrated Climate Based Daylight Modeling and building energy simulation (CBDM-BES). Based on the Sandia method, the proposed TMY generation leverages building energy simulation and NSGA-II algorithm to iteratively optimize weighting scheme assignment for better energy prediction during the generation process. Monthly and annual deviations of multiple energy consumption parameters from long-term average performance are applied as multi-objective functions. An application example of Hong Kong indicates that, with the generated TMY file, simulated results of multiple energy consumption parameters are simultaneously close to the long-term average on both the monthly and annual basis. The proposed TMY generation method is found effective in generating the feasible TMY file for CBDM-BES. The workflow of the proposed TMY generation also facilitates it to be embedded as a module in future architectural parametric design.

Suggested Citation

  • Sun, Jingting & Li, Zhengrong & Xiao, Fu & Xiao, Jianzhuang, 2020. "Generation of typical meteorological year for integrated climate based daylight modeling and building energy simulation," Renewable Energy, Elsevier, vol. 160(C), pages 721-729.
    • Handle: RePEc:eee:renene:v:160:y:2020:i:c:p:721-729
    DOI: 10.1016/j.renene.2020.07.024
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    References listed on IDEAS

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    Cited by:

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    2. Kunhao Liu & Lianglin Zou & Yuanlong Li & Kai Wang & Haiyu Wang & Jifeng Song, 2023. "Measurement and Analysis of Light Leakage in Plastic Optical Fiber Daylighting System," Sustainability, MDPI, vol. 15(4), pages 1-14, February.
    3. Zhang, Wenhao & Li, Honglian & Wang, Mengli & Lv, Wen & Huang, Jin & Yang, Liu, 2024. "Enhancing typical Meteorological Year generation for diverse energy systems: A hybrid Sandia-machine learning approach," Renewable Energy, Elsevier, vol. 225(C).
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    5. Jahns, Christopher & Osinski, Paul & Weber, Christoph, 2023. "A statistical approach to modeling the variability between years in renewable infeed on energy system level," Energy, Elsevier, vol. 263(PA).

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