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Energy efficiency of end-user groups for personalized HVAC control in multi-zone buildings

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  • Song, Kwonsik
  • Jang, Youjin
  • Park, Moonseo
  • Lee, Hyun-Soo
  • Ahn, Joseph

Abstract

HVAC control strategies have been considered as a means to reduce energy consumption in buildings. Personalizing the operation of HVAC systems depending on thermal zone could reduce energy consumption while minimizing the thermal discomfort of occupants in multi-zone buildings. Integrating smart metering technologies with building energy management systems in multi-zone buildings makes it possible to categorize individual rooms into several meaningful end-user groups (i.e., thermal zones) based on energy use behaviors. Unfortunately, it still remains unclear how energy efficient end-user groups are in multi-zone buildings. Therefore, this study evaluates the energy efficiency of end-user groups in multi-zone buildings. An energy efficiency assessment framework for end-user groups is developed using energy benchmarking data from seven dormitory buildings in Seoul, Korea. The results show that during the heating season, consistent energy users have the lowest energy efficiency among all end-user groups, especially during unoccupied hours. Also, the energy efficiency of nighttime-peak energy users is low when the outdoor dry-bulb temperature is higher than 0 °C. Last, the occupied buildings have a higher potential for energy savings at over 0 °C. The proposed framework could allow facility managers to determine appropriate control strategies for HVAC systems and personalize the control parameters depending on end-user group.

Suggested Citation

  • Song, Kwonsik & Jang, Youjin & Park, Moonseo & Lee, Hyun-Soo & Ahn, Joseph, 2020. "Energy efficiency of end-user groups for personalized HVAC control in multi-zone buildings," Energy, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:energy:v:206:y:2020:i:c:s0360544220312238
    DOI: 10.1016/j.energy.2020.118116
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    3. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
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    6. Huang, He & Wang, Honglei & Hu, Yu-Jie & Li, Chengjiang & Wang, Xiaolin, 2022. "Optimal plan for energy conservation and CO2 emissions reduction of public buildings considering users' behavior: Case of China," Energy, Elsevier, vol. 261(PA).

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