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An evaluation of empirically-based models for predicting energy performance of vapor-compression water chillers

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  • Lee, Tzong-Shing
  • Lu, Wan-Chen

Abstract

This paper presents an evaluation of six empirically-based models for predicting water chiller energy performance using over 1000 chiller data sets from chiller manufacturers and field measurements. The data sets comprise three broad classifications, including (1) constant condenser and constant chilled water flow, (2) constant condenser and variable chilled water flow, and (3) variable condenser and variable chilled water flow. The regression parameters for each performance model are obtained using least squares method. The criteria for evaluating the predictive ability of models are based on the coefficient of variation of root-mean-square error (CV). Results show that among the six empirically-based performance models for water chillers in this study, the bi-quadratic regression model (CVÂ =Â 2.2%) and the multivariate polynomial regression model (CVÂ =Â 2.25%) have the best prediction accuracy for all kinds of data sets. The results of this study can be used as a reference for selecting empirically-based models for the purposes of energy analysis, performance prediction, evaluation of energy-efficiency improvements, and fault detection and diagnosis of water chillers.

Suggested Citation

  • Lee, Tzong-Shing & Lu, Wan-Chen, 2010. "An evaluation of empirically-based models for predicting energy performance of vapor-compression water chillers," Applied Energy, Elsevier, vol. 87(11), pages 3486-3493, November.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:11:p:3486-3493
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    Cited by:

    1. Colorado, D. & Hernández, J.A. & García-Valladares, O. & Huicochea, A. & Siqueiros, J., 2011. "Numerical simulation and experimental validation of a helical double-pipe vertical condenser," Applied Energy, Elsevier, vol. 88(6), pages 2136-2145, June.
    2. Liu, Xuefeng & Huang, Bin & Zheng, Yulan, 2023. "Control strategy for dynamic operation of multiple chillers under random load constraints," Energy, Elsevier, vol. 270(C).
    3. Deymi-Dashtebayaz, Mahdi & Norani, Marziye, 2021. "Sustainability assessment and emergy analysis of employing the CCHP system under two different scenarios in a data center," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    4. 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.
    5. Powell, Kody M. & Cole, Wesley J. & Ekarika, Udememfon F. & Edgar, Thomas F., 2013. "Optimal chiller loading in a district cooling system with thermal energy storage," Energy, Elsevier, vol. 50(C), pages 445-453.
    6. Tipole, Pralhad & Karthikeyan, A. & Bhojwani, Virendra & Patil, Abhay & Oak, Ninad & Ponatil, Amal & Nagori, Palash, 2016. "Applying a magnetic field on liquid line of vapour compression system is a novel technique to increase a performance of the system," Applied Energy, Elsevier, vol. 182(C), pages 376-382.
    7. Thangavelu, Sundar Raj & Myat, Aung & Khambadkone, Ashwin, 2017. "Energy optimization methodology of multi-chiller plant in commercial buildings," Energy, Elsevier, vol. 123(C), pages 64-76.
    8. Lee, S.H. & Lee, W.L., 2013. "Site verification and modeling of desiccant-based system as an alternative to conventional air-conditioning systems for wet markets," Energy, Elsevier, vol. 55(C), pages 1076-1083.
    9. Chiam, Zhonglin & Papas, Ilias & Easwaran, Arvind & Alonso, Corinne & Estibals, Bruno, 2022. "Holistic optimization of the operation of a GCHP system: A case study on the ADREAM building in Toulouse, France," Applied Energy, Elsevier, vol. 321(C).
    10. Chiam, Zhonglin & Easwaran, Arvind & Mouquet, David & Fazlollahi, Samira & Millás, Jaume V., 2019. "A hierarchical framework for holistic optimization of the operations of district cooling systems," Applied Energy, Elsevier, vol. 239(C), pages 23-40.
    11. Blanca Foliaco & Antonio Bula & Peter Coombes, 2020. "Improving the Gordon-Ng Model and Analyzing Thermodynamic Parameters to Evaluate Performance in a Water-Cooled Centrifugal Chiller," Energies, MDPI, vol. 13(9), pages 1-20, April.
    12. Ding, Yan & Wang, Qiaochu & Kong, Xiangfei & Yang, Kun, 2019. "Multi-objective optimisation approach for campus energy plant operation based on building heating load scenarios," Applied Energy, Elsevier, vol. 250(C), pages 1600-1617.
    13. Chesi, Andrea & Ferrara, Giovanni & Ferrari, Lorenzo & Magnani, Sandro & Tarani, Fabio, 2013. "Influence of the heat storage size on the plant performance in a Smart User case study," Applied Energy, Elsevier, vol. 112(C), pages 1454-1465.
    14. Anjan Rao Puttige & Staffan Andersson & Ronny Östin & Thomas Olofsson, 2021. "Application of Regression and ANN Models for Heat Pumps with Field Measurements," Energies, MDPI, vol. 14(6), pages 1-26, March.
    15. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
    16. Lee, Tzong-Shing & Liao, Ke-Yang & Lu, Wan-Chen, 2012. "Evaluation of the suitability of empirically-based models for predicting energy performance of centrifugal water chillers with variable chilled water flow," Applied Energy, Elsevier, vol. 93(C), pages 583-595.
    17. Monfet, Danielle & Zmeureanu, Radu, 2012. "Ongoing commissioning of water-cooled electric chillers using benchmarking models," Applied Energy, Elsevier, vol. 92(C), pages 99-108.

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