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...
- Autores:
-
diaz torres, yamile
Gullo, Paride
Hernández Herrera, Hernán
Torres del Toro, Migdalia
Álvarez Guerra, Mario A.
Silva Ortega, Jorge I
Speerforck, Arne
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9841
- Acceso en línea:
- https://hdl.handle.net/11323/9841
https://repositorio.cuc.edu.co/
- Palabra clave:
- Chiller
Design variables
Energy saving
Life cycle cost
Pearson’s correlation
Spearman’s correlation
- Rights
- openAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
id |
RCUC2_92b006ebad1d9274e2f74892ea1db856 |
---|---|
oai_identifier_str |
oai:repositorio.cuc.edu.co:11323/9841 |
network_acronym_str |
RCUC2 |
network_name_str |
REDICUC - Repositorio CUC |
repository_id_str |
|
dc.title.eng.fl_str_mv |
Statistical analysis of design variables in a chiller plant and their influence on energy consumption and life cycle cost |
title |
Statistical analysis of design variables in a chiller plant and their influence on energy consumption and life cycle cost |
spellingShingle |
Statistical analysis of design variables in a chiller plant and their influence on energy consumption and life cycle cost Chiller Design variables Energy saving Life cycle cost Pearson’s correlation Spearman’s correlation |
title_short |
Statistical analysis of design variables in a chiller plant and their influence on energy consumption and life cycle cost |
title_full |
Statistical analysis of design variables in a chiller plant and their influence on energy consumption and life cycle cost |
title_fullStr |
Statistical analysis of design variables in a chiller plant and their influence on energy consumption and life cycle cost |
title_full_unstemmed |
Statistical analysis of design variables in a chiller plant and their influence on energy consumption and life cycle cost |
title_sort |
Statistical analysis of design variables in a chiller plant and their influence on energy consumption and life cycle cost |
dc.creator.fl_str_mv |
diaz torres, yamile Gullo, Paride Hernández Herrera, Hernán Torres del Toro, Migdalia Álvarez Guerra, Mario A. Silva Ortega, Jorge I Speerforck, Arne |
dc.contributor.author.none.fl_str_mv |
diaz torres, yamile Gullo, Paride Hernández Herrera, Hernán Torres del Toro, Migdalia Álvarez Guerra, Mario A. Silva Ortega, Jorge I Speerforck, Arne |
dc.subject.proposal.eng.fl_str_mv |
Chiller Design variables Energy saving Life cycle cost Pearson’s correlation Spearman’s correlation |
topic |
Chiller Design variables Energy saving Life cycle cost Pearson’s correlation Spearman’s correlation |
description |
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. |
publishDate |
2022 |
dc.date.issued.none.fl_str_mv |
2022-08-16 |
dc.date.accessioned.none.fl_str_mv |
2023-01-30T14:33:39Z |
dc.date.available.none.fl_str_mv |
2023-01-30T14:33:39Z |
dc.type.spa.fl_str_mv |
Artículo de revista |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.coarversion.spa.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
format |
http://purl.org/coar/resource_type/c_2df8fbb1 |
status_str |
publishedVersion |
dc.identifier.citation.spa.fl_str_mv |
: Torres, Y.D.; Gullo, P.; Herrera, H.H.; Torres del Toro, M.; Guerra, M.A.Á.; Ortega, J.I.S.; Speerforck, A. Statistical Analysis of Design Variables in a Chiller Plant and Their Influence on Energy Consumption and Life Cycle Cost. Sustainability 2022, 14, 10175. https://doi.org/10.3390/su141610175 |
dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/11323/9841 |
dc.identifier.doi.none.fl_str_mv |
10.3390/su141610175 |
dc.identifier.eissn.spa.fl_str_mv |
2071-1050 |
dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
identifier_str_mv |
: Torres, Y.D.; Gullo, P.; Herrera, H.H.; Torres del Toro, M.; Guerra, M.A.Á.; Ortega, J.I.S.; Speerforck, A. Statistical Analysis of Design Variables in a Chiller Plant and Their Influence on Energy Consumption and Life Cycle Cost. Sustainability 2022, 14, 10175. https://doi.org/10.3390/su141610175 10.3390/su141610175 2071-1050 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
url |
https://hdl.handle.net/11323/9841 https://repositorio.cuc.edu.co/ |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartofjournal.spa.fl_str_mv |
Sustainability |
dc.relation.references.spa.fl_str_mv |
1. ASHRAE. Fundamentals Handbook; ASHRAE: Atlanta, GA, USA, 2017; Chapter 18; ISBN 10-1939200598. 2. ASHRAE. Standard 62.2-2016 Ventilation for Acceptable Indoor Air Quality; ASHRAE: Atlanta, GA, USA, 2016; ISSN 1041-2336. 3. Norma Cubana NC 220-3:2009. Edificaciones-Requisitos de Diseño Para la Eficiencia Energética-Parte 3: Sistemas y Equipamiento de Calefaccion, Ventilación y Aire Acondicionado. Oficina Nacional de Normalización (NC). 2009. Available online: www.nc. cubaindustria.cu (accessed on 10 September 2020). 4. Nall, D. Rightsizing HVAC equipment. ASHRAE J. 2015, 57, 1. 5. Ruya, E.; Augenbroe, G. The impacts of HVAC downsizing on thermal comfort hours and energy consumption. In Proceedings of the ASHRAE and IBPSA-USA SimBuild Conference, Salt Lake City, UT, USA, 10–12 August 2016; Volume 6, p. 1. 6. Chang, Y.-C. A novel energy conservation method-optimal Chillers loading. Electr. Power Syst. Res. 2004, 69, 221–226. [CrossRef] 7. Menezes, A.C.; Cripps, A.; Buswell, R.A.; Wright, J.; Bouchlaghem, D. Estimating the energy consumption and power demand of small power equipment in office building. Energy Build. 2014, 75, 199–209. [CrossRef] 8. Wang, W.; Augenbroe, G.; Wang, S.; Fan, C.; Xiao, F. An uncertainty-based design optimization method for district cooling systems. Energy 2016, 102, 516–527. 9. Li, M.; Qin, C.; Feng, Y.; Zhou, M.; Mu, H.; Li, N.; Ma, Q. Optimal design and analysis of CCHP system for a hotel application. Energy Procedia 2017, 142, 2329–2334. [CrossRef] 10. Cheng, Q.; Wang, S.; Yan, C. Sequential Monte Carlo simulation for robust optimal design of cooling water system with quantified uncertainty and reliability. Energy 2017, 118, 489–501. [CrossRef] 11. Gang, W.; Wang, S.; Augenbroe, G.; Xiao, F. Robust optimal design of district cooling systems and the impacts of uncertainty and reliability. Energy Build. 2016, 122, 11–22. [CrossRef] 12. Cheng, Q.; Wang, S.; Yan, C.H.; Xiao, F. Probabilistic approach for uncertainty-based optimal design of chiller plants in buildings. Appl. Energy 2017, 185, 1613–1624. [CrossRef] 13. Kang, Y.; Augenbroe, Q.; Li, W.; Wang, Q. Effects of scenario uncertainty on chiller sizing method. Appl. Therm. Eng. 2017, 123, 187–195. [CrossRef] 14. Huang, P.; Huang, G.; Augenbroe, G.; Li, S. Optimal configuration of multiple-chiller plants under cooling load uncertainty for different climate effects and building types. Energy Build. 2018, 158, 684–697. [CrossRef] 15. Chai, J.; Huang, P.; Sun, Y. Life-cycle analysis of nearly zero energy buildings under uncertainty and degradation impacts for performance improvements. Energy Procedia 2019, 158, 2762–2767. [CrossRef] 16. Liao, Y.; Huang, G.; Ding, Y.; Wu, H.; Feng, Z. Robustness enhancement for chiller sequencing control under uncertainty. Appl. Therm. Eng. 2018, 141, 811–818. [CrossRef] 17. Yan, C.; Cheng, Q.; Cai, H. Life-Cycle optimization of a chiller plant with quantified analysis of uncertainty and reliability in commercial buildings. Appl. Sci. 2019, 9, 1548. [CrossRef] 18. Yu, F.W.; Ho, H.T. Assessing operating statuses for chiller system with Cox regression. Int. J. Refrig. 2019, 98, 182–193. [CrossRef] 19. Chen, Y.; Yang, C.; Pan, X.; Yan, D. Design and operation optimization of multi-chiller plants based on energy performance simulation. Energy Build. 2020, 222, 110100. [CrossRef] 20. Huang, P.; Huang, G.; Sun, Y. Uncertainty-based life-cycle analysis of near-zero energy buildings for performance improvements. Appl. Energy 2018, 213, 486–498. [CrossRef] 21. Vasisht, S.; Bhattacharya, A.; Huang, S.; Sharma, H.; Adetola, V.; Vrabie, D. Co-Design of Commercial Building HVAC using Bayesian Optimization. In Proceedings of the 2021 American Control Conference (ACC), New Orleans, LA, USA, 25–28 May 2021; pp. 366–372. 22. Kapoor, K.; Edgar, T. Chapter 2: Energy efficient chiller configuration. A design Perspective. Comput. Aided Chem. Eng. 2015, 36, 37–52. 23. Stanford, H.W., III. HVAC Water Chillers and Cooling Towers. Fundamentals, Application, and Operation, 2nd ed.; Marcel Dekker, Inc.: New York, NY, USA, 2016; ISBN 0-8247-0992-6. 24. Valentina, S.; Nasri, S.M.; Herespatiagni, R. Analysis of the Effect of HVAC System Modification towards Indoor Air Quality (IAQ) and Microbiological Growth at Accommodation and Office Buildings in an Oil and Gas Industry. J. Presipitasi Media Komun. Dan Pengemb. Tek. Lingkung. 2021, 18, 306–316. [CrossRef] 25. Hong, Y.; Ezeh, C.I.; Deng, W.; Hong, S.-H.; Peng, Z.; Tang, Y. Correlation between building characteristic and associated energy consumption: Prototyping low-rise office building in Shanghai. Energy Build. 2020, 217, 109959. [CrossRef] 26. Lee, W.L.; Lee, S.H. Developing a simplified model for evaluating chiller-system configurations. Appl. Energy 2007, 84, 290–306. [CrossRef] 27. Gang, W.; Wang, S.; Xiao, F.; Gao, D. Robust optimal design of building cooling systems considering cooling load uncertainty and equipment reliability. Appl. Energy 2015, 159, 265–275. [CrossRef] 28. Catrini, P.; Piacentino, A.; Cardona, F.; Ciulla, G. Exergoeconomic analysis as support in decision-making for the design and operation of multiple chiller systems in air conditioning applications. Energy Convers. Manag. 2020, 220, 113051. [CrossRef] 29. Maasoumy, M.; Zhu, Q.; Li, C.; Meggers, F.; Sangiovanni-Vincentelli, A. Co-design of control algorithm and embedded platform for building hvac systems. In Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), Philadelphia, PA, USA, 8–11 April 2013; pp. 61–70. 30. Bhattacharya, A.; Vasisht, S.; Adetola, V.; Huang, S.; Sharma, H.; Draguna, L. Control co-design of commercial building chiller plant using Bayesian optimization. Energy Build. 2021, 246, 111077. [CrossRef] 31. Ngyen, A.-T.; Reiter, S.; Rigo, P. A review on simulation based optimization methods applied to building performance analysis. Appl. Energy 2014, 113, 1043–1058. [CrossRef] 32. Díaz Torres, Y.; Álvarez Guerra, M.; Haeseldonck, D. The air conditioning system. Aspects that regulate their design for building. Part 2. Univ. Soc. 2020, 12, 461–469. 33. Díaz Torres, Y. Procedimiento Para Determinar la Distribución de la Capacidad Frigorífica Optima de Una Planta de Enfriadoras. Doctoral Thesis, Universidad de Cienfuegos, Cienfuegos, Cuba, 2021. 34. Díaz Torres, Y.; Reyes, C.R.; Hernandez, H.; Alvarez-Guerra, M.; Gómez, S.J.; Silva, I. Procedure to obtain the optimal distribution cooling capacity of an air-condensed chiller plant for a hotel facility conceptual design. Energy Rep. 2021, 7, 622–637. [CrossRef] 35. Díaz Torres, Y.R.; Hernandez, H.; Torres, M.; Alvarez-Guerra, M.; Gullo, P.; Silva, I. Statistical-mathematical procedure to determine the cooling distribution of a chiller plant. Energy Rep. 2022, 8, 512–526. [CrossRef] 36. Rendón, M.E.; Villasís, M.Á.; Miranda, M.G. Estadística descriptiva. Rev. Alerg. Mex. 2016, 63, 397–407. 37. Shapiro, S.S.; Wilk, M.B. An analysis of variance test for normality (complete samples). Biometrika 1965, 52, 591–611. [CrossRef] 38. Anderson, T.W.; Darling, D.A. Asymptotic theory of certain “goodness-of-fit” criteria based on stochastic processes. Ann. Math. Stat. 1952, 23, 193–212. [CrossRef] 39. Anderson, T.W.; Darling, D.A. A Test of Goodness-of-Fit. J. Am. Stat. Assoc. 1954, 49, 765–769. [CrossRef] 40. Kolmogorov, A. Sulla determinazione empirica di una legge di distribuzione. G. Dell’istituto Ital. Degli Attuari 1933, 4, 83–91. 41. Smirnov, N. Table for estimating the goodness of fit of empirical distributions. Ann. Math. Stat. 1948, 19, 279–281. [CrossRef] 42. Pearson, K. Contribution to the mathematical theory of evolution. II. Skew variation in homogenous material. Philos. Trans. R. Soc. Lond. 1895, 91, 343–414. 43. Cramér, H. On the composition of elementary errors. Scand. Actuar. J. 1928, 1, 13–74. [CrossRef] 44. Von Mises, R.E. Wahrscheinlichkeit, Statistik und Wahrheit; Julius Springer: Berlin, Germany, 1928. 45. Lilliefors, H.W. On the Kolmogorov-Smirnov test for normality with mean and variance unknown. J. Am. Stat. Assoc. 1967, 62, 399–400. [CrossRef] 46. Jarque, C.M.; Bera, A.K. Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Econ. Lett. 1980, 6, 255–259. [CrossRef] 47. Jarque, C.M.; Bera, A.K. Efficient tests for normality, homoscedasticity and serial independence of regression residuals: Monte Carlo evidence. Econ. Lett. 1981, 7, 313–318. 48. Jarque, C.M.; Bera, A.K. A test for normality of observations and regression residuals. Int. Stat. Rev. 1987, 55, 163–172. [CrossRef] 49. D’Agostino, R.B.; Belanger, A.; D’Agostino, R.B., Jr. A suggestion for using powerful and informative tests of normality. Am. Stat. 1990, 44, 316–321. 50. Hernandez, H. Testing for normality: What is the best method? Fors. Chem. Res. Rep. 2021, 6, 101–138. 51. Razali, N.M.; Wah, Y.B. Power comparisons of Shapiro–Wilk, Kolmogorov–Smirnov, Lilliefors and Anderson–Darling tests. J. Stat. Modeling Anal. 2011, 2, 21–33. 52. Demir, S. Comparison of Normality Tests in Terms of Sample Sizes under Different Skewness and Kurtosis Coefficients. Int. J. Assess. Tools Educ. 2022, 9, 397–409. [CrossRef] 53. Levene, H. Robust testes for equality of variances. In Contributions to Probability and Statistics; Olkin, I., Ed.; Stanford University Press: Palo Alto, CA, USA, 1960; pp. 278–292, MR0120709. 54. Bartlett, M.S. Properties of Sufficiency and Statistical Tests. Proc. R. Stat. Soc. 1937, 160, 268–282. 55. Cochran, W.G. Some consequences when the assumptions for the analysis of variance are not satisfied. Biometics 1947, 3, 22–38. [CrossRef] 56. Brown, M.B.; Forsythe, A.B. Robust tests for equality of variances. J. Am. Stat. Assoc. 1974, 69, 364–367. [CrossRef] 57. Vorapongsathorn, T.; Taejaroenkul, S.; Viwatwongkasem, C. A comparison of type I error and power of Bartlett’s test, Levene’s test and Cochran’s test under violation of assumptions. Sci. Technol. 2004, 26, 538–547. 58. Hernandez, J.D.; Espinoza, F.; Rodriguez, J.; Chacon, J.G.; Toloza, C.R.; Arenas, M.K.; Carrillo, S.M.; Pirela, V. On the proper use of the pearson correlation coefficient: Definitions, properties and assumption. Arch. Venez. Farmacol. Ter. 2018, 37, 552–561. 59. Asuero, A.G.; Sayago, A.; González, A.G. Correlation Coefficient: An Overview. Crit. Rev. Anal. Chem. 2006, 36, 41–59. [CrossRef] 60. Hair, J.F., Jr.; Money, A.H.; Samouel, P.; Page, M. Research Methods for Business; John Wiley & Sons: Hoboken, NJ, USA, 2007; Available online: https://digitalcommons.kennesaw.edu/facpubs/2952 (accessed on 25 January 2022). 61. Park, R.E. Estimation with Heteroscedastic Error Terms. Econometrica 1966, 34, 888. [CrossRef] 62. Glejser, H. A New Test for Heteroscedasticity. J. Am. Stat. Assoc. 1969, 64, 316–323. [CrossRef] 63. Goldfield, S.M.; Quandt, R.E. Some Test for Homoscedasticity. J. Am. Stat. Assoc. 1965, 310, 539–547. [CrossRef] 64. Harrison, M.J.; McCabe, P. A Test for Heteroscedasticity Based on Ordinary Least Square Residuals. J. Am. Stat. Assoc. 1979, 74, 494–499. 65. Breusch, T.S.; Pagan, A. The Review of Economic Studies. In Econometrics Issue; Oxford University Press: Oxford, UK, 1980; Volume 47, pp. 239–253. 66. White, H. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 1980, 48, 817–838. [CrossRef] 67. Koenker, R.; Bassett, G. Robust Tests for Heteroscedasticity Based on Regression Quantiles. Econometrica 1982, 50, 43–61. [CrossRef] 68. Onifade, O.C.; Olanrewaju, S.O. Investigating Performances of Some Statistical Tests for Heteroscedasticity Assumption in Generalized Linear Model: A Monte Carlo Simulations Study. Open J. Stat. 2020, 10, 453–493. [CrossRef] 69. Rasmussen, J.L.; Dunlap, W.P. Dealing with non-normal data: Parametric analysis of transformed data vs nonparametric analysis.Educ. Psychol. Meas. 1991, 51, 809–820. [CrossRef] 70. Olvera, O.L.O.; Zumbo, B.D. Heteroskedasticity in multiple Regression analysis: What it is, how to detect it and how to solve it with applications in R and SPSS. Pract. Assess. Res. Eval. 2019, 24, 1. 71. Huber, P.J. The behavior of maximum likelihood estimates under nonstandard conditions. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, USA, 7 January 1967; Volume 1, pp. 221–223. 72. Eicker, F. Limit theorems for regressions with unequal and dependent errors. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, USA, 7 January 1967; Volume 1, pp. 59–82. 73. Davidson, R.; Flachaire, E. The wild bootstrap, tamed at last. J. Econom. 2008, 146, 162–169. [CrossRef] 74. Flachaire, E. A better way to bootstrap pairs. Econ. Lett. 1999, 64, 257–262. [CrossRef] 75. Flachaire, E. Bootstrapping heteroskedastic regression models: Wild bootstrap vs. pairs bootstrap. Comput. Stat. Data Anal. 2005, 49, 361–376. [CrossRef] 76. Godfrey, L.G.; Orme, C.D. Controlling the finite sample significance levels of heteroskedasticity-robust tests of several linear restrictions on regression coefficients. Econ. Lett. 2004, 82, 281–287. [CrossRef] 77. MacKinnon, J.G. Thirty years of heteroskedasticity-robust inference. In Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis; Chen, X., Swanson, N.R.E., Eds.; Springer: Berlin/Heidelberg, Germany, 2013; pp. 437–462. 78. Richard, P. Robust heteroskedasticity-robust tests. Econ. Lett. 2017, 159, 28–32. [CrossRef] 79. Spearman, C. The proof and measurement of association between two things. Am. J. Psychol. 1904, 15, 72–101. [CrossRef] 80. Kendall, M.G. A new measure of rank correlation. Biometrika 1938, 30, 81–93. [CrossRef] 81. Statgraphics Centurion 18. Available online: https://www.statgraphics.com/download18 (accessed on 10 May 2022). 82. E-View 12 Student Version. Available online: https://www.eviews.com/home.html (accessed on 12 May 2022). 83. Scott Long, J.; Ervin Laurie, H. Using Heteroscedasticity Consistent Standard Errors in the Linear Regression Model. Am. Stat. 2000, 54, 217–224. 84. Johnson, N.L.; Kotz, S.; Balakrishnan, N. Chi-Square Distributions including Chi and Rayleigh. In Continuous Univariate Distributions, 2nd ed.; John Wiley and Sons: Hoboken, NJ, USA, 1994; Volume 1, pp. 415–493. ISBN 978-0-471-58495-7. 85. IBM SPSS Statistics 20. Available online: https://www.ibm.com/support/pages/downloading-ibm-spss-statistics-20 (accessed on 12 May 2022). 86. Huang, P.; Wang, Y.; Huang, G.; Augenbroe, G. Investigation of the ageing effect on chiller plant maximum cooling capacity using Bayesian Markov Chain Monte Carlo method. J. Build. Perform. Simul. 2016, 9, 529–541. [CrossRef] |
dc.relation.citationendpage.spa.fl_str_mv |
19 |
dc.relation.citationstartpage.spa.fl_str_mv |
1 |
dc.relation.citationissue.spa.fl_str_mv |
16 |
dc.relation.citationvolume.spa.fl_str_mv |
14 |
dc.rights.eng.fl_str_mv |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. |
dc.rights.license.spa.fl_str_mv |
Atribución 4.0 Internacional (CC BY 4.0) |
dc.rights.uri.spa.fl_str_mv |
https://creativecommons.org/licenses/by/4.0/ |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
rights_invalid_str_mv |
Atribución 4.0 Internacional (CC BY 4.0) © 2022 by the authors. Licensee MDPI, Basel, Switzerland. https://creativecommons.org/licenses/by/4.0/ http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.format.extent.spa.fl_str_mv |
19 páginas |
dc.format.mimetype.spa.fl_str_mv |
application/pdf |
dc.publisher.spa.fl_str_mv |
MDPI AG |
dc.publisher.place.spa.fl_str_mv |
Switzerland |
dc.source.spa.fl_str_mv |
https://www.mdpi.com/2071-1050/14/16/10175 |
institution |
Corporación Universidad de la Costa |
bitstream.url.fl_str_mv |
https://repositorio.cuc.edu.co/bitstreams/20d9d55b-faaf-43d0-8212-c861f7312562/download https://repositorio.cuc.edu.co/bitstreams/0b8ebeb1-6717-4fe1-b826-5b49a36c8bd9/download https://repositorio.cuc.edu.co/bitstreams/6f126e64-38f2-4855-9427-8d707d4420d2/download https://repositorio.cuc.edu.co/bitstreams/84865fe0-ac08-4269-9deb-054371142a1e/download |
bitstream.checksum.fl_str_mv |
056af83d3729639131aef01732ef72a5 2f9959eaf5b71fae44bbf9ec84150c7a ab4719ab9748732e9d9cabacd42903f2 9da499ad85c83752fcd8fce990fb9f2d |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositorio de la Universidad de la Costa CUC |
repository.mail.fl_str_mv |
repdigital@cuc.edu.co |
_version_ |
1811760666818641920 |
spelling |
Atribución 4.0 Internacional (CC BY 4.0)© 2022 by the authors. Licensee MDPI, Basel, Switzerland.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2diaz torres, yamileGullo, ParideHernández Herrera, HernánTorres del Toro, MigdaliaÁlvarez Guerra, Mario A.Silva Ortega, Jorge ISpeerforck, Arne2023-01-30T14:33:39Z2023-01-30T14:33:39Z2022-08-16: Torres, Y.D.; Gullo, P.; Herrera, H.H.; Torres del Toro, M.; Guerra, M.A.Á.; Ortega, J.I.S.; Speerforck, A. Statistical Analysis of Design Variables in a Chiller Plant and Their Influence on Energy Consumption and Life Cycle Cost. Sustainability 2022, 14, 10175. https://doi.org/10.3390/su141610175https://hdl.handle.net/11323/984110.3390/su1416101752071-1050Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.19 páginasapplication/pdfengMDPI AGSwitzerlandhttps://www.mdpi.com/2071-1050/14/16/10175Statistical analysis of design variables in a chiller plant and their influence on energy consumption and life cycle costArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Sustainability1. ASHRAE. Fundamentals Handbook; ASHRAE: Atlanta, GA, USA, 2017; Chapter 18; ISBN 10-1939200598.2. ASHRAE. Standard 62.2-2016 Ventilation for Acceptable Indoor Air Quality; ASHRAE: Atlanta, GA, USA, 2016; ISSN 1041-2336.3. Norma Cubana NC 220-3:2009. Edificaciones-Requisitos de Diseño Para la Eficiencia Energética-Parte 3: Sistemas y Equipamiento de Calefaccion, Ventilación y Aire Acondicionado. Oficina Nacional de Normalización (NC). 2009. Available online: www.nc. cubaindustria.cu (accessed on 10 September 2020).4. Nall, D. Rightsizing HVAC equipment. ASHRAE J. 2015, 57, 1.5. Ruya, E.; Augenbroe, G. The impacts of HVAC downsizing on thermal comfort hours and energy consumption. In Proceedings of the ASHRAE and IBPSA-USA SimBuild Conference, Salt Lake City, UT, USA, 10–12 August 2016; Volume 6, p. 1.6. Chang, Y.-C. A novel energy conservation method-optimal Chillers loading. Electr. Power Syst. Res. 2004, 69, 221–226. [CrossRef]7. Menezes, A.C.; Cripps, A.; Buswell, R.A.; Wright, J.; Bouchlaghem, D. Estimating the energy consumption and power demand of small power equipment in office building. Energy Build. 2014, 75, 199–209. [CrossRef]8. Wang, W.; Augenbroe, G.; Wang, S.; Fan, C.; Xiao, F. An uncertainty-based design optimization method for district cooling systems. Energy 2016, 102, 516–527.9. Li, M.; Qin, C.; Feng, Y.; Zhou, M.; Mu, H.; Li, N.; Ma, Q. Optimal design and analysis of CCHP system for a hotel application. Energy Procedia 2017, 142, 2329–2334. [CrossRef]10. Cheng, Q.; Wang, S.; Yan, C. Sequential Monte Carlo simulation for robust optimal design of cooling water system with quantified uncertainty and reliability. Energy 2017, 118, 489–501. [CrossRef]11. Gang, W.; Wang, S.; Augenbroe, G.; Xiao, F. Robust optimal design of district cooling systems and the impacts of uncertainty and reliability. Energy Build. 2016, 122, 11–22. [CrossRef]12. Cheng, Q.; Wang, S.; Yan, C.H.; Xiao, F. Probabilistic approach for uncertainty-based optimal design of chiller plants in buildings. Appl. Energy 2017, 185, 1613–1624. [CrossRef]13. Kang, Y.; Augenbroe, Q.; Li, W.; Wang, Q. Effects of scenario uncertainty on chiller sizing method. Appl. Therm. Eng. 2017, 123, 187–195. [CrossRef]14. Huang, P.; Huang, G.; Augenbroe, G.; Li, S. Optimal configuration of multiple-chiller plants under cooling load uncertainty for different climate effects and building types. Energy Build. 2018, 158, 684–697. [CrossRef]15. Chai, J.; Huang, P.; Sun, Y. Life-cycle analysis of nearly zero energy buildings under uncertainty and degradation impacts for performance improvements. Energy Procedia 2019, 158, 2762–2767. [CrossRef]16. Liao, Y.; Huang, G.; Ding, Y.; Wu, H.; Feng, Z. Robustness enhancement for chiller sequencing control under uncertainty. Appl. Therm. Eng. 2018, 141, 811–818. [CrossRef]17. Yan, C.; Cheng, Q.; Cai, H. Life-Cycle optimization of a chiller plant with quantified analysis of uncertainty and reliability in commercial buildings. Appl. Sci. 2019, 9, 1548. [CrossRef]18. Yu, F.W.; Ho, H.T. Assessing operating statuses for chiller system with Cox regression. Int. J. Refrig. 2019, 98, 182–193. [CrossRef]19. Chen, Y.; Yang, C.; Pan, X.; Yan, D. Design and operation optimization of multi-chiller plants based on energy performance simulation. Energy Build. 2020, 222, 110100. [CrossRef]20. Huang, P.; Huang, G.; Sun, Y. Uncertainty-based life-cycle analysis of near-zero energy buildings for performance improvements. Appl. Energy 2018, 213, 486–498. [CrossRef]21. Vasisht, S.; Bhattacharya, A.; Huang, S.; Sharma, H.; Adetola, V.; Vrabie, D. Co-Design of Commercial Building HVAC using Bayesian Optimization. In Proceedings of the 2021 American Control Conference (ACC), New Orleans, LA, USA, 25–28 May 2021; pp. 366–372.22. Kapoor, K.; Edgar, T. Chapter 2: Energy efficient chiller configuration. A design Perspective. Comput. Aided Chem. Eng. 2015, 36, 37–52.23. Stanford, H.W., III. HVAC Water Chillers and Cooling Towers. Fundamentals, Application, and Operation, 2nd ed.; Marcel Dekker, Inc.: New York, NY, USA, 2016; ISBN 0-8247-0992-6.24. Valentina, S.; Nasri, S.M.; Herespatiagni, R. Analysis of the Effect of HVAC System Modification towards Indoor Air Quality (IAQ) and Microbiological Growth at Accommodation and Office Buildings in an Oil and Gas Industry. J. Presipitasi Media Komun. Dan Pengemb. Tek. Lingkung. 2021, 18, 306–316. [CrossRef]25. Hong, Y.; Ezeh, C.I.; Deng, W.; Hong, S.-H.; Peng, Z.; Tang, Y. Correlation between building characteristic and associated energy consumption: Prototyping low-rise office building in Shanghai. Energy Build. 2020, 217, 109959. [CrossRef]26. Lee, W.L.; Lee, S.H. Developing a simplified model for evaluating chiller-system configurations. Appl. Energy 2007, 84, 290–306. [CrossRef]27. Gang, W.; Wang, S.; Xiao, F.; Gao, D. Robust optimal design of building cooling systems considering cooling load uncertainty and equipment reliability. Appl. Energy 2015, 159, 265–275. [CrossRef]28. Catrini, P.; Piacentino, A.; Cardona, F.; Ciulla, G. Exergoeconomic analysis as support in decision-making for the design and operation of multiple chiller systems in air conditioning applications. Energy Convers. Manag. 2020, 220, 113051. [CrossRef]29. Maasoumy, M.; Zhu, Q.; Li, C.; Meggers, F.; Sangiovanni-Vincentelli, A. Co-design of control algorithm and embedded platform for building hvac systems. In Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), Philadelphia, PA, USA, 8–11 April 2013; pp. 61–70.30. Bhattacharya, A.; Vasisht, S.; Adetola, V.; Huang, S.; Sharma, H.; Draguna, L. Control co-design of commercial building chiller plant using Bayesian optimization. Energy Build. 2021, 246, 111077. [CrossRef]31. Ngyen, A.-T.; Reiter, S.; Rigo, P. A review on simulation based optimization methods applied to building performance analysis. Appl. Energy 2014, 113, 1043–1058. [CrossRef]32. Díaz Torres, Y.; Álvarez Guerra, M.; Haeseldonck, D. The air conditioning system. Aspects that regulate their design for building. Part 2. Univ. Soc. 2020, 12, 461–469.33. Díaz Torres, Y. Procedimiento Para Determinar la Distribución de la Capacidad Frigorífica Optima de Una Planta de Enfriadoras. Doctoral Thesis, Universidad de Cienfuegos, Cienfuegos, Cuba, 2021.34. Díaz Torres, Y.; Reyes, C.R.; Hernandez, H.; Alvarez-Guerra, M.; Gómez, S.J.; Silva, I. Procedure to obtain the optimal distribution cooling capacity of an air-condensed chiller plant for a hotel facility conceptual design. Energy Rep. 2021, 7, 622–637. [CrossRef]35. Díaz Torres, Y.R.; Hernandez, H.; Torres, M.; Alvarez-Guerra, M.; Gullo, P.; Silva, I. Statistical-mathematical procedure to determine the cooling distribution of a chiller plant. Energy Rep. 2022, 8, 512–526. [CrossRef]36. Rendón, M.E.; Villasís, M.Á.; Miranda, M.G. Estadística descriptiva. Rev. Alerg. Mex. 2016, 63, 397–407.37. Shapiro, S.S.; Wilk, M.B. An analysis of variance test for normality (complete samples). Biometrika 1965, 52, 591–611. [CrossRef]38. Anderson, T.W.; Darling, D.A. Asymptotic theory of certain “goodness-of-fit” criteria based on stochastic processes. Ann. Math. Stat. 1952, 23, 193–212. [CrossRef]39. Anderson, T.W.; Darling, D.A. A Test of Goodness-of-Fit. J. Am. Stat. Assoc. 1954, 49, 765–769. [CrossRef]40. Kolmogorov, A. Sulla determinazione empirica di una legge di distribuzione. G. Dell’istituto Ital. Degli Attuari 1933, 4, 83–91.41. Smirnov, N. Table for estimating the goodness of fit of empirical distributions. Ann. Math. Stat. 1948, 19, 279–281. [CrossRef]42. Pearson, K. Contribution to the mathematical theory of evolution. II. Skew variation in homogenous material. Philos. Trans. R. Soc. Lond. 1895, 91, 343–414.43. Cramér, H. On the composition of elementary errors. Scand. Actuar. J. 1928, 1, 13–74. [CrossRef]44. Von Mises, R.E. Wahrscheinlichkeit, Statistik und Wahrheit; Julius Springer: Berlin, Germany, 1928.45. Lilliefors, H.W. On the Kolmogorov-Smirnov test for normality with mean and variance unknown. J. Am. Stat. Assoc. 1967, 62, 399–400. [CrossRef]46. Jarque, C.M.; Bera, A.K. Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Econ. Lett. 1980, 6, 255–259. [CrossRef]47. Jarque, C.M.; Bera, A.K. Efficient tests for normality, homoscedasticity and serial independence of regression residuals: Monte Carlo evidence. Econ. Lett. 1981, 7, 313–318.48. Jarque, C.M.; Bera, A.K. A test for normality of observations and regression residuals. Int. Stat. Rev. 1987, 55, 163–172. [CrossRef]49. D’Agostino, R.B.; Belanger, A.; D’Agostino, R.B., Jr. A suggestion for using powerful and informative tests of normality. Am. Stat. 1990, 44, 316–321.50. Hernandez, H. Testing for normality: What is the best method? Fors. Chem. Res. Rep. 2021, 6, 101–138.51. Razali, N.M.; Wah, Y.B. Power comparisons of Shapiro–Wilk, Kolmogorov–Smirnov, Lilliefors and Anderson–Darling tests. J. Stat. Modeling Anal. 2011, 2, 21–33.52. Demir, S. Comparison of Normality Tests in Terms of Sample Sizes under Different Skewness and Kurtosis Coefficients. Int. J. Assess. Tools Educ. 2022, 9, 397–409. [CrossRef]53. Levene, H. Robust testes for equality of variances. In Contributions to Probability and Statistics; Olkin, I., Ed.; Stanford University Press: Palo Alto, CA, USA, 1960; pp. 278–292, MR0120709.54. Bartlett, M.S. Properties of Sufficiency and Statistical Tests. Proc. R. Stat. Soc. 1937, 160, 268–282.55. Cochran, W.G. Some consequences when the assumptions for the analysis of variance are not satisfied. Biometics 1947, 3, 22–38. [CrossRef]56. Brown, M.B.; Forsythe, A.B. Robust tests for equality of variances. J. Am. Stat. Assoc. 1974, 69, 364–367. [CrossRef]57. Vorapongsathorn, T.; Taejaroenkul, S.; Viwatwongkasem, C. A comparison of type I error and power of Bartlett’s test, Levene’s test and Cochran’s test under violation of assumptions. Sci. Technol. 2004, 26, 538–547.58. Hernandez, J.D.; Espinoza, F.; Rodriguez, J.; Chacon, J.G.; Toloza, C.R.; Arenas, M.K.; Carrillo, S.M.; Pirela, V. On the proper use of the pearson correlation coefficient: Definitions, properties and assumption. Arch. Venez. Farmacol. Ter. 2018, 37, 552–561.59. Asuero, A.G.; Sayago, A.; González, A.G. Correlation Coefficient: An Overview. Crit. Rev. Anal. Chem. 2006, 36, 41–59. [CrossRef]60. Hair, J.F., Jr.; Money, A.H.; Samouel, P.; Page, M. Research Methods for Business; John Wiley & Sons: Hoboken, NJ, USA, 2007; Available online: https://digitalcommons.kennesaw.edu/facpubs/2952 (accessed on 25 January 2022).61. Park, R.E. Estimation with Heteroscedastic Error Terms. Econometrica 1966, 34, 888. [CrossRef]62. Glejser, H. A New Test for Heteroscedasticity. J. Am. Stat. Assoc. 1969, 64, 316–323. [CrossRef]63. Goldfield, S.M.; Quandt, R.E. Some Test for Homoscedasticity. J. Am. Stat. Assoc. 1965, 310, 539–547. [CrossRef]64. Harrison, M.J.; McCabe, P. A Test for Heteroscedasticity Based on Ordinary Least Square Residuals. J. Am. Stat. Assoc. 1979, 74, 494–499.65. Breusch, T.S.; Pagan, A. The Review of Economic Studies. In Econometrics Issue; Oxford University Press: Oxford, UK, 1980; Volume 47, pp. 239–253.66. White, H. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 1980, 48, 817–838. [CrossRef]67. Koenker, R.; Bassett, G. Robust Tests for Heteroscedasticity Based on Regression Quantiles. Econometrica 1982, 50, 43–61. [CrossRef]68. Onifade, O.C.; Olanrewaju, S.O. Investigating Performances of Some Statistical Tests for Heteroscedasticity Assumption in Generalized Linear Model: A Monte Carlo Simulations Study. Open J. Stat. 2020, 10, 453–493. [CrossRef]69. Rasmussen, J.L.; Dunlap, W.P. Dealing with non-normal data: Parametric analysis of transformed data vs nonparametric analysis.Educ. Psychol. Meas. 1991, 51, 809–820. [CrossRef]70. Olvera, O.L.O.; Zumbo, B.D. Heteroskedasticity in multiple Regression analysis: What it is, how to detect it and how to solve it with applications in R and SPSS. Pract. Assess. Res. Eval. 2019, 24, 1.71. Huber, P.J. The behavior of maximum likelihood estimates under nonstandard conditions. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, USA, 7 January 1967; Volume 1, pp. 221–223.72. Eicker, F. Limit theorems for regressions with unequal and dependent errors. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, USA, 7 January 1967; Volume 1, pp. 59–82.73. Davidson, R.; Flachaire, E. The wild bootstrap, tamed at last. J. Econom. 2008, 146, 162–169. [CrossRef]74. Flachaire, E. A better way to bootstrap pairs. Econ. Lett. 1999, 64, 257–262. [CrossRef]75. Flachaire, E. Bootstrapping heteroskedastic regression models: Wild bootstrap vs. pairs bootstrap. Comput. Stat. Data Anal. 2005, 49, 361–376. [CrossRef]76. Godfrey, L.G.; Orme, C.D. Controlling the finite sample significance levels of heteroskedasticity-robust tests of several linear restrictions on regression coefficients. Econ. Lett. 2004, 82, 281–287. [CrossRef]77. MacKinnon, J.G. Thirty years of heteroskedasticity-robust inference. In Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis; Chen, X., Swanson, N.R.E., Eds.; Springer: Berlin/Heidelberg, Germany, 2013; pp. 437–462.78. Richard, P. Robust heteroskedasticity-robust tests. Econ. Lett. 2017, 159, 28–32. [CrossRef]79. Spearman, C. The proof and measurement of association between two things. Am. J. Psychol. 1904, 15, 72–101. [CrossRef]80. Kendall, M.G. A new measure of rank correlation. Biometrika 1938, 30, 81–93. [CrossRef]81. Statgraphics Centurion 18. Available online: https://www.statgraphics.com/download18 (accessed on 10 May 2022).82. E-View 12 Student Version. Available online: https://www.eviews.com/home.html (accessed on 12 May 2022).83. Scott Long, J.; Ervin Laurie, H. Using Heteroscedasticity Consistent Standard Errors in the Linear Regression Model. Am. Stat. 2000, 54, 217–224.84. Johnson, N.L.; Kotz, S.; Balakrishnan, N. Chi-Square Distributions including Chi and Rayleigh. In Continuous Univariate Distributions, 2nd ed.; John Wiley and Sons: Hoboken, NJ, USA, 1994; Volume 1, pp. 415–493. ISBN 978-0-471-58495-7.85. IBM SPSS Statistics 20. Available online: https://www.ibm.com/support/pages/downloading-ibm-spss-statistics-20 (accessed on 12 May 2022).86. Huang, P.; Wang, Y.; Huang, G.; Augenbroe, G. Investigation of the ageing effect on chiller plant maximum cooling capacity using Bayesian Markov Chain Monte Carlo method. J. Build. Perform. Simul. 2016, 9, 529–541. [CrossRef]1911614ChillerDesign variablesEnergy savingLife cycle costPearson’s correlationSpearman’s correlationPublicationORIGINALStatistical analysis of design variables in a chiller plant and their influence on energy consumption and life cycle cost.pdfStatistical analysis of design variables in a chiller plant and their influence on energy consumption and life cycle cost.pdfArtículoapplication/pdf763016https://repositorio.cuc.edu.co/bitstreams/20d9d55b-faaf-43d0-8212-c861f7312562/download056af83d3729639131aef01732ef72a5MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/0b8ebeb1-6717-4fe1-b826-5b49a36c8bd9/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTStatistical analysis of design variables in a chiller plant and their influence on energy consumption and life cycle cost.pdf.txtStatistical analysis of design variables in a chiller plant and their influence on energy consumption and life cycle cost.pdf.txtExtracted texttext/plain76969https://repositorio.cuc.edu.co/bitstreams/6f126e64-38f2-4855-9427-8d707d4420d2/downloadab4719ab9748732e9d9cabacd42903f2MD53THUMBNAILStatistical analysis of design variables in a chiller plant and their influence on energy consumption and life cycle cost.pdf.jpgStatistical analysis of design variables in a chiller plant and their influence on energy consumption and life cycle cost.pdf.jpgGenerated Thumbnailimage/jpeg16172https://repositorio.cuc.edu.co/bitstreams/84865fe0-ac08-4269-9deb-054371142a1e/download9da499ad85c83752fcd8fce990fb9f2dMD5411323/9841oai:repositorio.cuc.edu.co:11323/98412024-09-16 16:39:07.138https://creativecommons.org/licenses/by/4.0/© 2022 by the authors. Licensee MDPI, Basel, Switzerland.open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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 |