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Heating Control Strategy Based on Dynamic Programming for Building Energy Saving and Emission Reduction

Author

Listed:
  • Haosen Qin

    (Tianjin Key Laboratory of Clean Energy and Pollutant Control, School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 400301, China)

  • Zhen Yu

    (Institute of Building Environment and Energy, China Academy of Building Research, Beijing 100013, China)

  • Tailu Li

    (Tianjin Key Laboratory of Clean Energy and Pollutant Control, School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 400301, China)

  • Xueliang Liu

    (Institute of Building Environment and Energy, China Academy of Building Research, Beijing 100013, China)

  • Li Li

    (Institute of Building Environment and Energy, China Academy of Building Research, Beijing 100013, China)

Abstract

Finding the optimal balance between end-user’s comfort, lifestyle preferences and the cost of the heating, ventilation and air conditioning (HVAC) system, which requires intelligent decision making and control. This paper proposes a heating control method for HVAC based on dynamic programming. The method first selects the most suitable modeling approach for the controlled building among three machine learning modeling techniques by means of statistical performance metrics, after which the control of the HVAC system is described as a constrained optimization problem, and the action of the controller is given by solving the optimization problem through dynamic programming. In this paper, the variable ‘thermal energy storage in building’ is introduced to solve the problem that dynamic programming is difficult to obtain the historical state of the building due to the requirement of no aftereffect, while the room temperature and the remaining start hours of the Primary Air Unit are selected to describe the system state through theoretical analysis and trial and error. The results of the TRNSYS/Python co-simulation show that the proposed method can maintain better indoor thermal environment with less energy consumption compared to carefully reviewed expert rules. Compared with expert rule set ‘baseline-20 °C’, which keeps the room temperature at the minimum comfort level, the proposed control algorithm can save energy and reduce emissions by 35.1% with acceptable comfort violation.

Suggested Citation

  • Haosen Qin & Zhen Yu & Tailu Li & Xueliang Liu & Li Li, 2022. "Heating Control Strategy Based on Dynamic Programming for Building Energy Saving and Emission Reduction," IJERPH, MDPI, vol. 19(21), pages 1-27, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:14137-:d:957205
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    References listed on IDEAS

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    Cited by:

    1. Abdelali Agouzoul & Emmanuel Simeu & Mohamed Tabaa, 2024. "Advancing Sustainable Building Practices: Intelligent Methods for Enhancing Heating and Cooling Energy Efficiency," Sustainability, MDPI, vol. 16(7), pages 1-29, March.
    2. Qin, Haosen & Yu, Zhen & Li, Tailu & Liu, Xueliang & Li, Li, 2023. "Energy-efficient heating control for nearly zero energy residential buildings with deep reinforcement learning," Energy, Elsevier, vol. 264(C).

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