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Model-based energy monitoring and diagnosis of telecommunication cooling systems

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  • Sorrentino, Marco
  • Acconcia, Matteo
  • Panagrosso, Davide
  • Trifirò, Alena

Abstract

A methodology is proposed for on-line monitoring of cooling load supplied by Telecommunication (TLC) cooling systems. Sensible cooling load is estimated via a proportional integral controller-based input estimator, whereas a lumped parameters model was developed aiming at estimating air handling units (AHUs) latent heat load removal. The joint deployment of above estimators enables accurate prediction of total cooling load, as well as of related AHUs and free-coolers energy performance. The procedure was then proven effective when extended to cooling systems having a centralized chiller, through model-based estimation of a key performance metric, such as the energy efficiency ratio.

Suggested Citation

  • Sorrentino, Marco & Acconcia, Matteo & Panagrosso, Davide & Trifirò, Alena, 2016. "Model-based energy monitoring and diagnosis of telecommunication cooling systems," Energy, Elsevier, vol. 116(P1), pages 761-772.
    • Handle: RePEc:eee:energy:v:116:y:2016:i:p1:p:761-772
    DOI: 10.1016/j.energy.2016.10.028
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    References listed on IDEAS

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    Cited by:

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    6. Dong, Zhe & Li, Bowen & Li, Junyi & Huang, Xiaojin & Zhang, Zuoyi, 2022. "Online reliability assessment of energy systems based on a high-order extended-state-observer with application to nuclear reactors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    7. Hou, Juan & Li, Haoran & Nord, Natasa, 2022. "Nonlinear model predictive control for the space heating system of a university building in Norway," Energy, Elsevier, vol. 253(C).
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    9. Nastro, Francesco & Sorrentino, Marco & Trifirò, Alena, 2022. "A machine learning approach based on neural networks for energy diagnosis of telecommunication sites," Energy, Elsevier, vol. 245(C).
    10. Taneja, Shivani & Mandys, Filip, 2022. "The effect of disaggregated information and communication technologies on industrial energy demand," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).

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