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Time-aware chiller sequencing control with data-driven chiller performance profiling

Published:08 November 2017Publication History

ABSTRACT

To fulfill the cooling demand in commercial buildings, the operation of air conditioning system with multiple chillers accounts for the major proportion of electricity consumption. However, the performances of operation strategies are varied under different status and configurations of chiller systems. Accordingly, decisions to optimize the chiller sequencing are becoming ever so complex that run-time effective and prompt control algorithms must play a key role. In this poster, a time-aware chiller sequencing control with data-driven strategy was proposed to maximize the energy saving while ensuring the physical chiller operation time. Evaluation shows that our approach achieves 12.91% improvement in total power costs of the tested three years.

References

  1. Kody M Powell, Wesley J Cole, et al. 2013. Optimal chiller loading in a district cooling system with thermal energy storage. Energy 50 (2013), 445--453.Google ScholarGoogle ScholarCross RefCross Ref
  2. Yongjun Sun et al. 2009. Chiller sequencing control with enhanced robustness for energy efficient operation. Energy and buildings 41, 11 (2009), 1246--1255.Google ScholarGoogle Scholar
  3. Vakiloroaya et al. 2013. Energy-efficient HVAC systems: Simulation-empirical modelling and gradient optimization. Automation in Construction 31 (2013), 176--185.Google ScholarGoogle ScholarCross RefCross Ref
  4. Liping Xiang. 2009. Theoretical Studies on Start-Up Characteristics of a High-Pressure Generator for LiBr Absorption Chiller. In APPEEC'09. IEEE, 1--4.Google ScholarGoogle ScholarCross RefCross Ref

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  • Published in

    cover image ACM Conferences
    BuildSys '17: Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments
    November 2017
    292 pages
    ISBN:9781450355445
    DOI:10.1145/3137133

    Copyright © 2017 Owner/Author

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 8 November 2017

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    Overall Acceptance Rate148of500submissions,30%
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