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A Fully Distributed Genetic Algorithm for Global Optimization of HVAC Systems

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Advancements in Smart City and Intelligent Building (ICSCIB 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 890 ))

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Abstract

To solve the high labor and maintenance cost problems in actual engineering, a decentralized heating, ventilation, and air-conditioning (HVAC) system is configured following its physical layout. In a decentralized HVAC control system, each of the updated smart equipment can communicate with the adjacent nodes collaboratively to fulfill the load requirement. Furthermore, to achieve the global optimal operation of an HVAC system, a fully distributed constrained optimization is formulated. In this paper, a fully distributed genetic algorithm (GA) is developed to solve the proposed constrained optimization. The proposed method is confirmed to be effective to realize the global optimization of HVAC system through simulation study.

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Acknowledgements

This work is supported by the National Key Research and Development Project of China No. 2017YFC0704100 (entitled New Generation Intelligent Building Platform Techniques).

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Authors and Affiliations

  1. School of Defense Engineering, Army Engineering University of PLA, Nanjing, Jiangsu, China

    Shiqiang Wang, Jianchun Xing & Juelong Li

Authors
  1. Shiqiang Wang
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  2. Jianchun Xing
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  3. Juelong Li
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Corresponding author

Correspondence to Jianchun Xing .

Editor information

Editors and Affiliations

  1. Anhui Jianzhu University, Hefei, China

    Qiansheng Fang

  2. University of the West of England, Bristol, UK

    Quanmin Zhu

  3. Shenyang Jianzhu University, Shenyang, China

    Feng Qiao

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Wang, S., Xing, J., Li, J. (2019). A Fully Distributed Genetic Algorithm for Global Optimization of HVAC Systems. In: Fang, Q., Zhu, Q., Qiao, F. (eds) Advancements in Smart City and Intelligent Building. ICSCIB 2018. Advances in Intelligent Systems and Computing, vol 890 . Springer, Singapore. https://doi.org/10.1007/978-981-13-6733-5_57

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  • DOI: https://doi.org/10.1007/978-981-13-6733-5_57

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6732-8

  • Online ISBN: 978-981-13-6733-5

  • eBook Packages: EngineeringEngineering (R0)

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