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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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    References listed on IDEAS

    as
    1. Mossolly, M. & Ghali, K. & Ghaddar, N., 2009. "Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm," Energy, Elsevier, vol. 34(1), pages 58-66.
    2. Motlagh, Omid & Paevere, Phillip & Hong, Tang Sai & Grozev, George, 2015. "Analysis of household electricity consumption behaviours: Impact of domestic electricity generation," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 165-178.
    3. Oldewurtel, Frauke & Sturzenegger, David & Morari, Manfred, 2013. "Importance of occupancy information for building climate control," Applied Energy, Elsevier, vol. 101(C), pages 521-532.
    4. Yang, Liu & Yan, Haiyan & Lam, Joseph C., 2014. "Thermal comfort and building energy consumption implications – A review," Applied Energy, Elsevier, vol. 115(C), pages 164-173.
    5. Goyal, Siddharth & Ingley, Herbert A. & Barooah, Prabir, 2013. "Occupancy-based zone-climate control for energy-efficient buildings: Complexity vs. performance," Applied Energy, Elsevier, vol. 106(C), pages 209-221.
    6. Shimoda, Yoshiyuki & Asahi, Takahiro & Taniguchi, Ayako & Mizuno, Minoru, 2007. "Evaluation of city-scale impact of residential energy conservation measures using the detailed end-use simulation model," Energy, Elsevier, vol. 32(9), pages 1617-1633.
    7. Yun, Geun Young & Steemers, Koen, 2011. "Behavioural, physical and socio-economic factors in household cooling energy consumption," Applied Energy, Elsevier, vol. 88(6), pages 2191-2200, June.
    8. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    9. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    10. Fadzli Haniff, Mohamad & Selamat, Hazlina & Yusof, Rubiyah & Buyamin, Salinda & Sham Ismail, Fatimah, 2013. "Review of HVAC scheduling techniques for buildings towards energy-efficient and cost-effective operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 94-103.
    11. Rahman, M.M. & Rasul, M.G. & Khan, M.M.K., 2010. "Energy conservation measures in an institutional building in sub-tropical climate in Australia," Applied Energy, Elsevier, vol. 87(10), pages 2994-3004, October.
    12. Zeng, Yaohui & Zhang, Zijun & Kusiak, Andrew, 2015. "Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms," Energy, Elsevier, vol. 86(C), pages 393-402.
    13. Song, Kwonsik & Kim, Sooyoung & Park, Moonseo & Lee, Hyun-Soo, 2017. "Energy efficiency-based course timetabling for university buildings," Energy, Elsevier, vol. 139(C), pages 394-405.
    14. Chicco, Gianfranco, 2012. "Overview and performance assessment of the clustering methods for electrical load pattern grouping," Energy, Elsevier, vol. 42(1), pages 68-80.
    15. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2016. "Occupancy-based demand response and thermal comfort optimization in microgrids with renewable energy sources and energy storage," Applied Energy, Elsevier, vol. 163(C), pages 93-104.
    16. Sehar, Fakeha & Pipattanasomporn, Manisa & Rahman, Saifur, 2016. "A peak-load reduction computing tool sensitive to commercial building environmental preferences," Applied Energy, Elsevier, vol. 161(C), pages 279-289.
    17. Liu, Jiangyan & Chen, Huanxin & Liu, Jiahui & Li, Zhengfei & Huang, Ronggeng & Xing, Lu & Wang, Jiangyu & Li, Guannan, 2017. "An energy performance evaluation methodology for individual office building with dynamic energy benchmarks using limited information," Applied Energy, Elsevier, vol. 206(C), pages 193-205.
    Full references (including those not matched with items on IDEAS)

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    5. Dhowmya Bhatt & Danalakshmi D & A. Hariharasudan & Marcin Lis & Marlena Grabowska, 2021. "Forecasting of Energy Demands for Smart Home Applications," Energies, MDPI, vol. 14(4), pages 1-19, February.
    6. Waseem, Muhammad & Lin, Zhenzhi & Liu, Shengyuan & Zhang, Zhi & Aziz, Tarique & Khan, Danish, 2021. "Fuzzy compromised solution-based novel home appliances scheduling and demand response with optimal dispatch of distributed energy resources," Applied Energy, Elsevier, vol. 290(C).
    7. 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).
    8. 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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