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A model-based control strategy to recover cooling energy from thermal mass in commercial buildings

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  • Shan, Kui
  • Wang, Jiayuan
  • Hu, Maomao
  • Gao, Dian-ce

Abstract

Building structures and furniture have the capability of storing thermal energy and therefore could be called building thermal mass. In commercial buildings, room conditions remain in thermal comfort zone for a while after the end of office hours when the air-conditioning systems are tuned off. It is possible to recover the stored cooling energy from building thermal mass in commercial buildings. This study proposes a model-based control strategy for such purpose. A simplified building RC model and a black-box model are combined by a simple data fusion algorithm for easier implementation and more accuracy prediction. The feasibility of such energy recovery was validated on-site in a super high-rise commercial building in Hong Kong, and the proposed method was validated on a dynamic simulation platform built based on the same building. In the two on-site validations, the energy savings during the energy recovering period were 85.8% and 80.1%, respectively. In the simulation tests, by allowing indoor air temperature increase by 1 K, the proposed control strategy could save 83.8% of the cooling energy during the recovering period, which accounts for 7.23% of the total cooling energy consumption in the entire tested days.

Suggested Citation

  • Shan, Kui & Wang, Jiayuan & Hu, Maomao & Gao, Dian-ce, 2019. "A model-based control strategy to recover cooling energy from thermal mass in commercial buildings," Energy, Elsevier, vol. 172(C), pages 958-967.
    • Handle: RePEc:eee:energy:v:172:y:2019:i:c:p:958-967
    DOI: 10.1016/j.energy.2019.02.045
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    References listed on IDEAS

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    Cited by:

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    2. Li, Yanfei & O'Neill, Zheng & Zhang, Liang & Chen, Jianli & Im, Piljae & DeGraw, Jason, 2021. "Grey-box modeling and application for building energy simulations - A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    3. Ashraf Balabel & Mamdooh Alwetaishi, 2021. "Towards Sustainable Residential Buildings in Saudi Arabia According to the Conceptual Framework of “Mostadam” Rating System and Vision 2030," Sustainability, MDPI, vol. 13(2), pages 1-16, January.
    4. Lin, Xiaojie & Zhang, Junwei & Du-Ikonen, Liuliu & Zhong, Wei, 2023. "An infiltration load calculation model of large-space buildings based on the grand canonical ensemble theory," Energy, Elsevier, vol. 275(C).
    5. Joe, Jaewan & Im, Piljae & Cui, Borui & Dong, Jin, 2023. "Model-based predictive control of multi-zone commercial building with a lumped building modelling approach," Energy, Elsevier, vol. 263(PA).
    6. Hawks, M.A. & Cho, S., 2024. "Review and analysis of current solutions and trends for zero energy building (ZEB) thermal systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    7. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu, 2023. "Globally optimal control of hybrid chilled water plants integrated with small-scale thermal energy storage for energy-efficient operation," Energy, Elsevier, vol. 262(PA).

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