Statistical analysis of design variables in a chiller plant and their influence on energy consumption and life cycle cost

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.

Subjects

chiller

design variables

energy saving

life cycle cost

Pearson’s correlation

Spearman’s correlation

DDC Class

530: Physik

Funding Organisations

Study center of energy and environment

Cienfuegos Tourism

Publication version

publishedVersion

Lizenz

https://creativecommons.org/licenses/by/4.0/

Name

sustainability-14-10175-v3.pdf

Size

745.13 KB

Format

Adobe PDF

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Statistical analysis of design variables in a chiller plant and their influence on energy consumption and life cycle cost