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Statistical Analysis of Design Variables in a Chiller Plant and Their Influence on Energy Consumption and Life Cycle Cost

Author

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  • Yamile Díaz Torres

    (Department of Engineering and Exact Sciences, Instituto Superior Politécnico “Alvorecer da Juventude”, Urbanição Nova Vida, Rua 45. Kilamba Kiaxi, Luanda P.O. Box 583, Angola)

  • Paride Gullo

    (Department of Mechanical and Electrical Engineering, University of Southern Denmark (SDU), 6400 Sønderborg, Denmark)

  • Hernán Hernández Herrera

    (Faculty of Engineering, Universidad Simón Bolivar, Barranquilla 080005, Colombia)

  • Migdalia Torres del Toro

    (Department of Engineering and Exact Sciences, Instituto Superior Politécnico “Alvorecer da Juventude”, Urbanição Nova Vida, Rua 45. Kilamba Kiaxi, Luanda P.O. Box 583, Angola)

  • Mario A. Álvarez Guerra

    (Studies Center for Energy and Environment, Universidad Carlos Rafael Rodríguez, Cienfuegos 55100, Cuba)

  • Jorge Iván Silva Ortega

    (Department of Energy, Universidad de la Costa, Barranquilla 080005, Colombia)

  • Arne Speerforck

    (Institute of Engineering Thermodynamics, Hamburg University of Technology, 21073 Hamburg, Germany)

Abstract

An appropriate design of a chiller plant is crucial to guarantee highly performing solutions. However, several design variables, such as type of systems, total cooling capacity, and hydraulic arrangement, need to be considered. On the one hand, at present, different technical criteria for selecting the most suitable design variables are available. Studies that corroborate the influence of the design variables over the operational variables are missing. In order to fill this knowledge gap, this work proposes a statistical analysis of design variables in chiller plants operating in medium- and large-scale applications and evaluates their influence on energy consumption and life cycle cost (LCC) under the same thermal demand conditions. A case study involving 138 chiller plant combinations featuring different arrangements and a Cuban hotel was selected. The results suggested that the total chiller design and cooling capacity distribution among chillers have a significant influence on the energy consumption of the chiller plant with a Spearman’s Rho and Kendall Tau ( τ ) correlation index value of −0.625 and 0.559, respectively. However, with LCC, only the cooling capacity distribution among the chillers had a certain influence with a Kendall Tau correlation index value of 0.289. As for the considered total cooling capacity, the applied statistical test showed that this design variable does not have any influence on performing the chiller plant.

Suggested Citation

  • Yamile Díaz Torres & Paride Gullo & Hernán Hernández Herrera & Migdalia Torres del Toro & Mario A. Álvarez Guerra & Jorge Iván Silva Ortega & Arne Speerforck, 2022. "Statistical Analysis of Design Variables in a Chiller Plant and Their Influence on Energy Consumption and Life Cycle Cost," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10175-:d:889719
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    References listed on IDEAS

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    Cited by:

    1. Mohamed Salah Benkhalfallah & Sofia Kouah & Saad Harous, 2025. "Predicting the Energy Consumption in Chillers: A Comparative Study of Supervised Machine Learning Regression Models," Energies, MDPI, vol. 18(14), pages 1-26, July.
    2. Marylin Santander-Bossio & Jorge Silva-Ortega & Ruben Cantero-Rodelo & Prince Torres-Salazar & Juan Rivera-Alvarado & Christian Moreno-Rocha & Celene Milánes-Batista, 2025. "From Waste to Energy: Cooking Oil Recycling for Biodiesel in Barranquilla, Colombia," Sustainability, MDPI, vol. 17(14), pages 1-17, July.
    3. Fu-Wing Yu & Wai-Tung Ho, 2023. "Time Series Forecast of Cooling Demand for Sustainable Chiller System in an Office Building in a Subtropical Climate," Sustainability, MDPI, vol. 15(8), pages 1-18, April.

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