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Ongoing commissioning of water-cooled electric chillers using benchmarking models

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  • Monfet, Danielle
  • Zmeureanu, Radu

Abstract

This paper proposes two different types of benchmark models for the comparison of energy performance of water-cooled electric chillers: correlation-based models and Artificial Neural Network (ANN) models. Different techniques are proposed to establish the models and are evaluated with data collected from two chillers installed in an existing central cooling and heating plant. Both chillers have identical capacity and performance characteristics; however, they have quite different operating hours. The results show that models developed in this case study with 7days of data monitored at the beginning of the summer season provide accurate results over the remaining of the summer and for the following summer. The proposed Multivariable Polynomial (MP) models for chillers provide the most accurate prediction with CV(RMSE) below 7% over the remaining of the summer season, and below 8% for the following summer season.

Suggested Citation

  • Monfet, Danielle & Zmeureanu, Radu, 2012. "Ongoing commissioning of water-cooled electric chillers using benchmarking models," Applied Energy, Elsevier, vol. 92(C), pages 99-108.
    • Handle: RePEc:eee:appene:v:92:y:2012:i:c:p:99-108
    DOI: 10.1016/j.apenergy.2011.10.019
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    References listed on IDEAS

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    1. Ginestet, S. & Marchio, D., 2010. "Retro and on-going commissioning tool applied to an existing building: Operability and results of IPMVP," Energy, Elsevier, vol. 35(4), pages 1717-1723.
    2. 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.
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    Cited by:

    1. Liang, Xinbin & Zhu, Xu & Chen, Siliang & Jin, Xinqiao & Xiao, Fu & Du, Zhimin, 2023. "Physics-constrained cooperative learning-based reference models for smart management of chillers considering extrapolation scenarios," Applied Energy, Elsevier, vol. 349(C).
    2. 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.
    3. Hongwen Dou & Radu Zmeureanu, 2022. "Detection and Diagnosis of Multiple-Dependent Faults (MDFDD) of Water-Cooled Centrifugal Chillers Using Grey-Box Model-Based Method," Energies, MDPI, vol. 16(1), pages 1-20, December.
    4. Behrad Bezyan & Radu Zmeureanu, 2020. "Machine Learning for Benchmarking Models of Heating Energy Demand of Houses in Northern Canada," Energies, MDPI, vol. 13(5), pages 1-20, March.
    5. Fan, Cheng & Sun, Yongjun & Shan, Kui & Xiao, Fu & Wang, Jiayuan, 2018. "Discovering gradual patterns in building operations for improving building energy efficiency," Applied Energy, Elsevier, vol. 224(C), pages 116-123.
    6. Ahn, Jonghoon & Cho, Soolyeon, 2017. "Anti-logic or common sense that can hinder machine’s energy performance: Energy and comfort control models based on artificial intelligence responding to abnormal indoor environments," Applied Energy, Elsevier, vol. 204(C), pages 117-130.
    7. Germán Ramos Ruiz & Carlos Fernández Bandera, 2017. "Validation of Calibrated Energy Models: Common Errors," Energies, MDPI, vol. 10(10), pages 1-19, October.

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