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Improved energy management of chiller systems by multivariate and data envelopment analyses

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  • Yu, F.W.
  • Chan, K.T.

Abstract

The operation of chiller systems accounts for the major proportion of electricity consumption in commercial buildings. This paper considers using multivariate and data envelopment analyses to facilitate the energy management of chiller systems. The system studied contains five sets of chillers, pumps and cooling waters and it operates for an institutional building. Based on a huge set of operating data, multiple linear regression was used to correlate the system coefficient of performance (COP) with a set of climatic and operating variables. Data envelopment analysis was then employed to calculate the scale, technical and overall efficiencies. These three efficiencies were further examined to ascertain which controllable variables caused a decrease of system COP. The results show that the existing energy management gives a technical efficiency of 0.76 and fine-tuning the controllable variables could achieve an electricity saving of 5.34% in relation to the existing operation. The significance of this study is to demonstrate a systematic approach to examine which operating variable should be fine-tuned to improve system performance with higher technical efficiency.

Suggested Citation

  • Yu, F.W. & Chan, K.T., 2012. "Improved energy management of chiller systems by multivariate and data envelopment analyses," Applied Energy, Elsevier, vol. 92(C), pages 168-174.
    • Handle: RePEc:eee:appene:v:92:y:2012:i:c:p:168-174
    DOI: 10.1016/j.apenergy.2011.11.016
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    1. Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2019. "Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm," Energies, MDPI, vol. 12(15), pages 1-13, July.
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    6. Serafín Alonso & Antonio Morán & Miguel Ángel Prada & Perfecto Reguera & Juan José Fuertes & Manuel Domínguez, 2019. "A Data-Driven Approach for Enhancing the Efficiency in Chiller Plants: A Hospital Case Study," Energies, MDPI, vol. 12(5), pages 1-28, March.
    7. Tirmizi, Syed A. & Gandhidasan, P. & Zubair, Syed M., 2012. "Performance analysis of a chilled water system with various pumping schemes," Applied Energy, Elsevier, vol. 100(C), pages 238-248.
    8. Nadimi, Reza & Tokimatsu, Koji, 2019. "Potential energy saving via overall efficiency relying on quality of life," Applied Energy, Elsevier, vol. 233, pages 283-299.
    9. Abou-Ziyan, Hosny Z. & Alajmi, Ali F., 2014. "Effect of load-sharing operation strategy on the aggregate performance of existed multiple-chiller systems," Applied Energy, Elsevier, vol. 135(C), pages 329-338.
    10. Xuefeng, Liu & Jinping, Liu & Zhitao, Lu & Kongzu, Xing & Yuebang, Mai, 2015. "Diversity of energy-saving control strategy for a parallel chilled water pump based on variable differential pressure control in an air-conditioning system," Energy, Elsevier, vol. 88(C), pages 718-733.
    11. Hong, Tianzhen & Yang, Le & Hill, David & Feng, Wei, 2014. "Data and analytics to inform energy retrofit of high performance buildings," Applied Energy, Elsevier, vol. 126(C), pages 90-106.
    12. Gerhard Zucker & Usman Habib & Max Blöchle & Florian Judex & Thomas Leber, 2015. "Sanitation and Analysis of Operation Data in Energy Systems," Energies, MDPI, vol. 8(11), pages 1-19, November.
    13. Chua, K.J. & Chou, S.K. & Yang, W.M. & Yan, J., 2013. "Achieving better energy-efficient air conditioning – A review of technologies and strategies," Applied Energy, Elsevier, vol. 104(C), pages 87-104.
    14. Deng, Jiewen & Wei, Qingpeng & Qian, Yangyang & Zhang, Hui, 2018. "Does magnetic bearing variable-speed centrifugal chiller perform truly energy efficient in buildings: Field-test and simulation results," Applied Energy, Elsevier, vol. 229(C), pages 998-1009.
    15. Chen, Qun & Wang, Yi-Fei & Xu, Yun-Chao, 2015. "A thermal resistance-based method for the optimal design of central variable water/air volume chiller systems," Applied Energy, Elsevier, vol. 139(C), pages 119-130.
    16. Wang, Yijun & Jin, Xinqiao & Shi, Wantao & Wang, Jiangqing, 2019. "Online chiller loading strategy based on the near-optimal performance map for energy conservation," Applied Energy, Elsevier, vol. 238(C), pages 1444-1451.
    17. Huang, Sen & Zuo, Wangda & Sohn, Michael D., 2016. "Amelioration of the cooling load based chiller sequencing control," Applied Energy, Elsevier, vol. 168(C), pages 204-215.
    18. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2020. "A risk-based robust optimal chiller sequencing control strategy for energy-efficient operation considering measurement uncertainties," Applied Energy, Elsevier, vol. 280(C).
    19. Ruparathna, Rajeev & Hewage, Kasun & Sadiq, Rehan, 2016. "Improving the energy efficiency of the existing building stock: A critical review of commercial and institutional buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1032-1045.
    20. Afroz, Zakia & Shafiullah, GM & Urmee, Tania & Higgins, Gary, 2018. "Modeling techniques used in building HVAC control systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 64-84.

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