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Energy-consumption simulation of a distributed air-conditioning system integrated with occupant behavior

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  • Xie, Jiantong
  • Pan, Yiqun
  • Jia, Wenqi
  • Xu, Lei
  • Huang, Zhizhong

Abstract

Occupant behavior (OB) has been recognized as a significant factor that influences the energy consumed by the occupants of a building. For buildings equipped with distributed air-conditioning systems, the stochastic influences of occupants are particularly salient. This paper presents a method for simulating the occupancy and air-conditioning usage; it integrates the OB model with a modified distributed air-conditioning system in EnergyPlus (E+). First, we develop a monitoring system that uses motion sensors and thermostats to measure the occupancy and air-conditioning usage in a hotel building. Then, we use the Markov Chain method and a Monte-Carlo stochastic model to simulate the occupancy and set-point adjustment, respectively. We modify the distributed air-conditioning system in E+ to reflect the intermittent operation and temperature fluctuation characteristics of split-type air conditioners (ACs). Finally, to demonstrate the applicability of the proposed method, we conduct a simulation of a hotel building that integrates the OB model with the modified distributed air-conditioning system. The results show that the method can incorporate the features of both the OB and the split-type ACs. Significant differences (7.86%) can be observed in the energy consumption results between the original and modified models. The modified E+ model can be used to perform a more accurate simulation for split-type ACs with a shorter time step, integrating OB at the scale of an entire building.

Suggested Citation

  • Xie, Jiantong & Pan, Yiqun & Jia, Wenqi & Xu, Lei & Huang, Zhizhong, 2019. "Energy-consumption simulation of a distributed air-conditioning system integrated with occupant behavior," Applied Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:appene:v:256:y:2019:i:c:s0306261919316010
    DOI: 10.1016/j.apenergy.2019.113914
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    References listed on IDEAS

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    1. Soltanaghaei, Elahe & Whitehouse, Kamin, 2018. "Practical occupancy detection for programmable and smart thermostats," Applied Energy, Elsevier, vol. 220(C), pages 842-855.
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    5. Gaetani, Isabella & Hoes, Pieter-Jan & Hensen, Jan L.M., 2018. "Estimating the influence of occupant behavior on building heating and cooling energy in one simulation run," Applied Energy, Elsevier, vol. 223(C), pages 159-171.
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    Cited by:

    1. Wahhaj Ahmed & Muhammad Asif & Farajallah Alrashed, 2019. "Application of Building Performance Simulation to Design Energy-Efficient Homes: Case Study from Saudi Arabia," Sustainability, MDPI, vol. 11(21), pages 1-16, October.
    2. Shiva Amirkhani & Ali Bahadori-Jahromi & Anastasia Mylona & Paulina Godfrey & Darren Cook, 2020. "Impact of Adding Comfort Cooling Systems on the Energy Consumption and EPC Rating of an Existing UK Hotel," Sustainability, MDPI, vol. 12(7), pages 1-16, April.
    3. Ana-Maria Nica & Andreea Raceanu & Mihail-Ovidiu Tanase & GabrielaTigu, 2023. "Managerial Challenges Related to the Efficient Use of Energy Resources in the Hotel Industry," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 25(64), pages 691-691, August.
    4. Kong, Xiangfei & Xi, Chang & Li, Han & Lin, Zhang, 2020. "Multi-parameter performance optimization for whole year operation of stratum ventilation in offices," Applied Energy, Elsevier, vol. 268(C).

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