Statistical-mathematical procedure to determine the cooling distribution of a chiller plant

This paper presents a procedure to determine the cooling capacity distribution of the chillers composing a chiller plant using a statistical analysis of the building cooling demand. The mathematical-statistical procedure uses tools such as frequency histograms, box-and-whisker plots, stem-and-leaf p...

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Published in:Energy Reports
Main Authors: Yamile Díaz Torres, Hernán Hernández Herrera, Migdalia Torres del Toro, Mario A. Álvarez Guerra, Paride Gullo, Jorge Iván Silva Ortega
Format: Article
Language:English
Published: Elsevier 2022-11-01
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2352484722012872
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author Yamile Díaz Torres
Hernán Hernández Herrera
Migdalia Torres del Toro
Mario A. Álvarez Guerra
Paride Gullo
Jorge Iván Silva Ortega
author_facet Yamile Díaz Torres
Hernán Hernández Herrera
Migdalia Torres del Toro
Mario A. Álvarez Guerra
Paride Gullo
Jorge Iván Silva Ortega
author_sort Yamile Díaz Torres
collection DOAJ
container_title Energy Reports
description This paper presents a procedure to determine the cooling capacity distribution of the chillers composing a chiller plant using a statistical analysis of the building cooling demand. The mathematical-statistical procedure uses tools such as frequency histograms, box-and-whisker plots, stem-and-leaf plots, the generalized least squares method, and finally an iterative factorial procedure to generate from the processed information. Besides the manufacturer’s data, all possible chiller plant combinations considering design constraints. The procedure was verified in a hotel facility. Eight thermal demand profiles were simulated. Statistical analysis yielded a range of individual capacities between 100–353 kW. The procedure generated 189 refrigeration plant combinations between 2 to 5 chillers, with a safety factor (SF) between 10%–20%. The highest number of combinations considered plants comprising three and four chillers, reaching 50 and 70 chiller plant options, respectively.
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spelling doaj-art-be4efa61d8ff4eeeae2aa2f09e1f150d2025-08-19T21:25:41ZengElsevierEnergy Reports2352-48472022-11-01851252610.1016/j.egyr.2022.07.023Statistical-mathematical procedure to determine the cooling distribution of a chiller plantYamile Díaz Torres0Hernán Hernández Herrera1Migdalia Torres del Toro2Mario A. Álvarez Guerra3Paride Gullo4Jorge Iván Silva Ortega5Departamento de ingeniería y ciencias exactas, Politécnico Superior “Alborecer da Juventude”, Calle 45, Reparto Nova Vida, Luanda, AngolaUniversidad Simón Bolívar, Facultad de Ingenierías, Barranquilla, Colombia; Corresponding author.Departamento de ingeniería y ciencias exactas, Politécnico Superior “Alborecer da Juventude”, Calle 45, Reparto Nova Vida, Luanda, AngolaStudy Center of Energy and Environment, University Carlos Rafael Rodríguez, Cienfuegos, CubaUniversity of Southern Denmark (SDU), Department of Mechanical and Electrical Engineering, Alsion 2, 6400 Sønderborg, DenmarkDepartamento de Energía, Universidad de la Costa, Barranquilla, ColombiaThis paper presents a procedure to determine the cooling capacity distribution of the chillers composing a chiller plant using a statistical analysis of the building cooling demand. The mathematical-statistical procedure uses tools such as frequency histograms, box-and-whisker plots, stem-and-leaf plots, the generalized least squares method, and finally an iterative factorial procedure to generate from the processed information. Besides the manufacturer’s data, all possible chiller plant combinations considering design constraints. The procedure was verified in a hotel facility. Eight thermal demand profiles were simulated. Statistical analysis yielded a range of individual capacities between 100–353 kW. The procedure generated 189 refrigeration plant combinations between 2 to 5 chillers, with a safety factor (SF) between 10%–20%. The highest number of combinations considered plants comprising three and four chillers, reaching 50 and 70 chiller plant options, respectively.http://www.sciencedirect.com/science/article/pii/S2352484722012872Chiller plantsStatistical-mathematical procedureChiller plant combinationsCooling capacityChillers
spellingShingle Yamile Díaz Torres
Hernán Hernández Herrera
Migdalia Torres del Toro
Mario A. Álvarez Guerra
Paride Gullo
Jorge Iván Silva Ortega
Statistical-mathematical procedure to determine the cooling distribution of a chiller plant
Chiller plants
Statistical-mathematical procedure
Chiller plant combinations
Cooling capacity
Chillers
title Statistical-mathematical procedure to determine the cooling distribution of a chiller plant
title_full Statistical-mathematical procedure to determine the cooling distribution of a chiller plant
title_fullStr Statistical-mathematical procedure to determine the cooling distribution of a chiller plant
title_full_unstemmed Statistical-mathematical procedure to determine the cooling distribution of a chiller plant
title_short Statistical-mathematical procedure to determine the cooling distribution of a chiller plant
title_sort statistical mathematical procedure to determine the cooling distribution of a chiller plant
topic Chiller plants
Statistical-mathematical procedure
Chiller plant combinations
Cooling capacity
Chillers
url http://www.sciencedirect.com/science/article/pii/S2352484722012872
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