IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i16p10175-d889719.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/16/10175/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/16/10175/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cheng, Qi & Wang, Shengwei & Yan, Chengchu & Xiao, Fu, 2017. "Probabilistic approach for uncertainty-based optimal design of chiller plants in buildings," Applied Energy, Elsevier, vol. 185(P2), pages 1613-1624.
    2. Flachaire, Emmanuel, 1999. "A better way to bootstrap pairs," Economics Letters, Elsevier, vol. 64(3), pages 257-262, September.
    3. Davidson, Russell & Flachaire, Emmanuel, 2008. "The wild bootstrap, tamed at last," Journal of Econometrics, Elsevier, vol. 146(1), pages 162-169, September.
    4. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    5. Gang, Wenjie & Wang, Shengwei & Xiao, Fu & Gao, Dian-ce, 2015. "Robust optimal design of building cooling systems considering cooling load uncertainty and equipment reliability," Applied Energy, Elsevier, vol. 159(C), pages 265-275.
    6. Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January.
    7. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    8. Flachaire, Emmanuel, 2005. "Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 361-376, April.
    9. Cheng, Qi & Wang, Shengwei & Yan, Chengchu, 2017. "Sequential Monte Carlo simulation for robust optimal design of cooling water system with quantified uncertainty and reliability," Energy, Elsevier, vol. 118(C), pages 489-501.
    10. Gang, Wenjie & Augenbroe, Godfried & Wang, Shengwei & Fan, Cheng & Xiao, Fu, 2016. "An uncertainty-based design optimization method for district cooling systems," Energy, Elsevier, vol. 102(C), pages 516-527.
    11. Godfrey, Leslie G. & Orme, Chris D., 2004. "Controlling the finite sample significance levels of heteroskedasticity-robust tests of several linear restrictions on regression coefficients," Economics Letters, Elsevier, vol. 82(2), pages 281-287, February.
    12. Richard, Patrick, 2017. "Robust heteroskedasticity-robust tests," Economics Letters, Elsevier, vol. 159(C), pages 28-32.
    13. Huang, Pei & Huang, Gongsheng & Sun, Yongjun, 2018. "Uncertainty-based life-cycle analysis of near-zero energy buildings for performance improvements," Applied Energy, Elsevier, vol. 213(C), pages 486-498.
    14. Nguyen, Anh-Tuan & Reiter, Sigrid & Rigo, Philippe, 2014. "A review on simulation-based optimization methods applied to building performance analysis," Applied Energy, Elsevier, vol. 113(C), pages 1043-1058.
    15. Bera, Anil K. & Jarque, Carlos M., 1981. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals : Monte Carlo Evidence," Economics Letters, Elsevier, vol. 7(4), pages 313-318.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pötscher, Benedikt M. & Preinerstorfer, David, 2023. "How Reliable Are Bootstrap-Based Heteroskedasticity Robust Tests?," Econometric Theory, Cambridge University Press, vol. 39(4), pages 789-847, August.
    2. Emmanuel Flachaire, 2005. "More Efficient Tests Robust to Heteroskedasticity of Unknown Form," Econometric Reviews, Taylor & Francis Journals, vol. 24(2), pages 219-241.
    3. Cyrus J. DiCiccio & Joseph P. Romano & Michael Wolf, 2016. "Improving weighted least squares inference," ECON - Working Papers 232, Department of Economics - University of Zurich, revised Nov 2017.
    4. Romano, Joseph P. & Wolf, Michael, 2017. "Resurrecting weighted least squares," Journal of Econometrics, Elsevier, vol. 197(1), pages 1-19.
    5. Godfrey, L.G., 2006. "Tests for regression models with heteroskedasticity of unknown form," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2715-2733, June.
    6. Torben Klarl, 2014. "Is Spatial Bootstrapping A Panacea For Valid Inference?," Journal of Regional Science, Wiley Blackwell, vol. 54(2), pages 304-312, March.
    7. Li, Hangxin & Wang, Shengwei, 2017. "Probabilistic optimal design concerning uncertainties and on-site adaptive commissioning of air-conditioning water pump systems in buildings," Applied Energy, Elsevier, vol. 202(C), pages 53-65.
    8. Godfrey, L.G. & Tremayne, A.R., 2005. "The wild bootstrap and heteroskedasticity-robust tests for serial correlation in dynamic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 377-395, April.
    9. DiCiccio, Cyrus J. & Romano, Joseph P. & Wolf, Michael, 2019. "Improving weighted least squares inference," Econometrics and Statistics, Elsevier, vol. 10(C), pages 96-119.
    10. Emmanuel Flachaire, 2000. "Les méthodes du bootstrap dans les modèles de régression," Économie et Prévision, Programme National Persée, vol. 142(1), pages 183-194.
    11. Adrian Pagan & Hashem Pesaran, 2007. "Econometric Analysis of Structural Systems with Permanent and Transitory Shocks. Working paper #7," NCER Working Paper Series 7, National Centre for Econometric Research.
    12. Adrian C. Darnell, 1994. "A Dictionary Of Econometrics," Books, Edward Elgar Publishing, number 118.
    13. Stan Hurn & Ralf Becker, 2009. "Testing for Nonlinearity in Mean in the Presence of Heteroskedasticity," Economic Analysis and Policy, Elsevier, vol. 39(2), pages 311-326, September.
    14. Philip Arestis & Ana Rosa Gonzalez‐Martinez, 2019. "Economic precariousness: A new channel in the housing market cycle," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(2), pages 1030-1043, April.
    15. Niu, Jide & Tian, Zhe & Yue, Lu, 2020. "Robust optimal design of building cooling sources considering the uncertainty and cross-correlation of demand and source," Applied Energy, Elsevier, vol. 265(C).
    16. Olivier Armantier, 2006. "Estimates of Own Lethal Risks and Anchoring Effects," Journal of Risk and Uncertainty, Springer, vol. 32(1), pages 37-56, January.
    17. Flachaire, Emmanuel, 2005. "Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 361-376, April.
    18. repec:lan:wpaper:2454 is not listed on IDEAS
    19. repec:lan:wpaper:2375 is not listed on IDEAS
    20. Jia, Zhiyang & Jin, Xinqiao & Lyu, Yuan & Xue, Qi & Du, Zhimin, 2023. "A robust capacity configuration selection method of multiple-chiller system concerned with the uncertainty of annual hourly load profile," Energy, Elsevier, vol. 282(C).
    21. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    22. Li, Hangxin & Wang, Shengwei & Tang, Rui, 2019. "Robust optimal design of zero/low energy buildings considering uncertainties and the impacts of objective functions," Applied Energy, Elsevier, vol. 254(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10175-:d:889719. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.