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Model-based multi-objective optimal control of a VRF (variable refrigerant flow) combined system with DOAS (dedicated outdoor air system) using genetic algorithm under heating conditions

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  • Kim, Wonuk
  • Jeon, Seung Won
  • Kim, Yongchan

Abstract

A VRF (variable refrigerant flow) combined system adopting a DOAS (dedicated outdoor air system) has been proposed to reduce the total energy consumption while satisfying IAQ (indoor air quality) and THC (thermal and humidity comfort) with minimum outdoor air. The objective of this study is to develop a model-based multi-objective optimal control strategy for the VRF combined system with multi-zone in order to optimize the multi-objective functions of the THC, IAQ, and total energy consumption. The performance of the VRF combined system was evaluated using the EnergyPlus model. The VRF combined system was optimized by GA (genetic algorithm) and RSM (response surface methodology) with the multi-objective functions of the THC, IAQ, and total energy consumption. The proposed multi-objective optimal control strategies (A and B) were compared with the TS (time schedule) strategy and the DCVH (demand controlled ventilation with humidifying). Optimal control strategy B reduced the total energy consumption by 20.4% and increased the ratio of the hours satisfying the extended comfort zone by 19.1% compared to the DCVH strategy.

Suggested Citation

  • Kim, Wonuk & Jeon, Seung Won & Kim, Yongchan, 2016. "Model-based multi-objective optimal control of a VRF (variable refrigerant flow) combined system with DOAS (dedicated outdoor air system) using genetic algorithm under heating conditions," Energy, Elsevier, vol. 107(C), pages 196-204.
    • Handle: RePEc:eee:energy:v:107:y:2016:i:c:p:196-204
    DOI: 10.1016/j.energy.2016.03.139
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    References listed on IDEAS

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    1. Jeon, Jongug & Lee, Sunil & Hong, Daehie & Kim, Yongchan, 2010. "Performance evaluation and modeling of a hybrid cooling system combining a screw water chiller with a ground source heat pump in a building," Energy, Elsevier, vol. 35(5), pages 2006-2012.
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    Cited by:

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    2. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2020. "Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system," Applied Energy, Elsevier, vol. 257(C).
    3. Yu, Min Gyung & Pavlak, Gregory S., 2022. "Extracting interpretable building control rules from multi-objective model predictive control data sets," Energy, Elsevier, vol. 240(C).
    4. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2019. "Adaptive full-range decoupled ventilation strategy and air-conditioning systems for cleanrooms and buildings requiring strict humidity control and their performance evaluation," Energy, Elsevier, vol. 168(C), pages 883-896.
    5. Zhang, Sheng & Sun, Yongjun & Cheng, Yong & Huang, Pei & Oladokun, Majeed Olaide & Lin, Zhang, 2018. "Response-surface-model-based system sizing for Nearly/Net zero energy buildings under uncertainty," Applied Energy, Elsevier, vol. 228(C), pages 1020-1031.
    6. Li, Wenzhuo & Wang, Shengwei, 2020. "A multi-agent based distributed approach for optimal control of multi-zone ventilation systems considering indoor air quality and energy use," Applied Energy, Elsevier, vol. 275(C).
    7. Hong, Ying-Yi & Chang, Wen-Chun & Chang, Yung-Ruei & Lee, Yih-Der & Ouyang, Der-Chuan, 2017. "Optimal sizing of renewable energy generations in a community microgrid using Markov model," Energy, Elsevier, vol. 135(C), pages 68-74.

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