Design optimization of an assisted living facility to improve summer thermal comfort in warming climates

Amaripadath, Deepak; Prasad, Deo; Safi, Taha Osman; Attia, Shady

doi:10.1016/j.jobe.2024.109814

Download

Article (Scientific journals)

Design optimization of an assisted living facility to improve summer thermal comfort in warming climates

Amaripadath, Deepak; Prasad, Deo; Safi, Taha Osman et al.

2024 • In Journal of Building Engineering, 93, p. 109814

Peer Reviewed verified by ORBi

Permalink
https://hdl.handle.net/2268/319250

DOI
10.1016/j.jobe.2024.109814

Files (1)Send to Details Statistics Bibliography Similar publications

Files

Full Text

Design optimization of an assisted living facility 2.pdf

Author preprint (5.16 MB)

Creative Commons License - Attribution, Non-Commercial, No Derivative

Download

All documents in ORBi are protected by a user license.

Send to

RIS BibTex APA Chicago Permalink X Linkedin

Details

Keywords :

Quality of life; senior homes; passive house; discomfort hours; sensitivity analysis; climate change

Abstract :

[en] Assisted living facilities with optimal summer thermal comfort are necessary to ensure improved quality of life for the older individuals in warming climates. Older individuals are exposed to higher mortality risks during periods of excessive heat. This study aims to identify and optimize key design variables for a passive house certified assisted living facility in Belgium with high comfort expectations using EnergyPlus to improve indoor thermal comfort during extreme climates. The sensitivity analysis used the Standard Regression Coefficient method with Latin Hypercube Sampling for eighteen design variables under four categories: layout, envelope, operation, and system. The analysis identified flat roof and external floor insulation as the most influential parameters, whereas lighting gain and cooling setback temperature were the least influential parameters. Furthermore, optimizing the reference building with the most sensitive design variables using a genetic algorithm based on the NSGA-II method found ideal configurations that would ensure minimum thermal discomfort hours during extreme summers. The study findings provide an evidence-based approach for building engineers and designers for early-stage design of assisted living facilities that maintain optimal comfort in mixed humid climates of Europe.

Research Center/Unit :

Sustainable Building Design Lab

Disciplines :

Architecture

Author, co-author :

Amaripadath, Deepak ; Université de Liège - ULiège > Urban and Environmental Engineering

Prasad, Deo

Safi, Taha Osman

Attia, Shady ; Université de Liège - ULiège > Département ArGEnCo > Techniques de construction des bâtiments

Language :

English

Title :

Design optimization of an assisted living facility to improve summer thermal comfort in warming climates

Publication date :

01 September 2024

Journal title :

Journal of Building Engineering

eISSN :

2352-7102

Publisher :

Elsevier BV

Volume :

Pages :

109814

Peer reviewed :

Peer Reviewed verified by ORBi

Development Goals :

3. Good health and well-being

Additional URL :

https://www.sciencedirect.com/science/article/pii/S2352710224013822?dgcid=coauthor

Name of the research project :

Surchauffe

Available on ORBi :

since 03 June 2024

Statistics

Number of views

88 (8 by ULiège)

Number of downloads

63 (1 by ULiège)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenAlex citations

Bibliography

Howe, P.D., Extreme weather experience and climate change opinion. Current Opinion in Behavioral Sciences 42 (Dec. 2021), 127–131, 10.1016/j.cobeha.2021.05.005.
Sheng, M., Reiner, M., Sun, K., Hong, T., Assessing thermal resilience of an assisted living facility during heat waves and cold snaps with power outages. Build. Environ., 230, Feb. 2023, 110001, 10.1016/j.buildenv.2023.110001.
R. Gupta, M. Vahanvati, J. Häggstrom, J. Halcomb, A practical guide to climate-resilient buildings and communities, United Nations Environment Programme (2020). Available at: https://www.unep.org/resources/practical-guide-climate-resilient-buildings, ISBN: 978-92-807-3871-1.
PHE. Heatwave Plan for England. 2020 London, UK.
Martin, J., Shochat, T., Ancoli-Israel, S., Assessment and treatment of sleep disturbances in older adults. Clin. Psychol. Rev. 20:6 (Aug. 2000), 783–805, 10.1016/S0272-7358(99)00063-X.
Gupta, R., et al. Monitoring and modelling the risk of summertime overheating and passive solutions to avoid active cooling in London care homes. Energy Build., 252, Dec. 2021, 111418, 10.1016/j.enbuild.2021.111418.
Eurostat, Population structure and ageing, Statistics Explained (2023) [Online] https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Population_structure_and_ageing. Accessed on: June 02, 2024.
Vossius, C., et al. Ressursbruk og sykdomsforløp ved demens (REDIC). Sykehuset Innlandet, Technical report, 2015, 1–155.
Vossius, C., Selbæk, G., Šaltytė Benth, J., Bergh, S., Mortality in nursing home residents: a longitudinal study over three years. PLoS One, 13(9), 2018, 10.1371/journal.pone.0203480.
Sakka, A., Santamouris, M., Livada, I., Nicol, F., Wilson, M., On the thermal performance of low income housing during heat waves. Energy Build. 49 (Jun. 2012), 69–77, 10.1016/j.enbuild.2012.01.023.
Ji, L., Laouadi, A., Shu, C., Gaur, A., Lacasse, M., Wang, L., Evaluating approaches of selecting extreme hot years for assessing building overheating conditions during heatwaves. Energy Build., 254, Jan. 2022, 111610, 10.1016/j.enbuild.2021.111610.
Klenk, J., Becker, C., Rapp, K., Heat-related mortality in residents of nursing homes. Age Ageing 39:2 (Mar. 2010), 245–252, 10.1093/ageing/afp248.
Kovats, R.S., Johnson, H., Griffith, C., Mortality in Southern England during the 2003 Heat Wave by Place of Death, vol. 29, 2006, Health statistics quarterly/Office for National Statistics, 6–8.
Hajat, S., Kovats, R.S., Lachowycz, K., Heat-related and cold-related deaths in England and Wales: who is at risk?. Occup. Environ. Med. 64:2 (2007), 93–100, 10.1136/oem.2006.029017.
Van den Wyngaert, I., et al. Impact of heat waves on hospitalisation and mortality in nursing homes: a case-crossover study. Int. J. Environ. Res. Publ. Health, 18(20), Oct. 2021, 10697, 10.3390/ijerph182010697.
State of Florida Agency for health care administration, recommended orders for rehabilitation. Center at Hollywood Hills LLC, 2019, 32 http://apps.ahca.myflorida.com/dm_web/DMWeb_DocsFO/9238299.pdf.
Sun, K., Specian, M., Hong, T., Nexus of thermal resilience and energy efficiency in buildings: a case study of a nursing home. Build. Environ., 177, Jun. 2020, 106842, 10.1016/j.buildenv.2020.106842.
Liu, P., Justo Alonso, M., Mathisen, H.M., Global sensitivity analysis and optimal design of heat recovery ventilation for zero emission buildings. Appl. Energy, 329, Jan. 2023, 120237, 10.1016/j.apenergy.2022.120237.
Li, H., Wang, S., Cheung, H., Sensitivity analysis of design parameters and optimal design for zero/low energy buildings in subtropical regions. Appl. Energy 228 (Oct. 2018), 1280–1291, 10.1016/j.apenergy.2018.07.023.
Zhao, Z., Li, H., Wang, S., Identification of the key design parameters of Zero/low energy buildings and the impacts of climate and building morphology. Appl. Energy, 328, Dec. 2022, 120185, 10.1016/j.apenergy.2022.120185.
ANSI/ASHRAE. ASHRAE Standard 169: Climatic Data for Building Design Standards. 2013, American Society of Heating, Refrigerating and Air Conditioning Engineers, Atlanta, GA, USA.
Prataviera, E., Vivian, J., Lombardo, G., Zarrella, A., Evaluation of the impact of input uncertainty on urban building energy simulations using uncertainty and sensitivity analysis. Appl. Energy, 311, Apr. 2022, 118691, 10.1016/j.apenergy.2022.118691.
Zhang, Y., Zhang, X., Huang, P., Sun, Y., Global sensitivity analysis for key parameters identification of net-zero energy buildings for grid interaction optimization. Appl. Energy, 279, Dec. 2020, 115820, 10.1016/j.apenergy.2020.115820.
Neale, J., Shamsi, M.H., Mangina, E., Finn, D., O'Donnell, J., Accurate identification of influential building parameters through an integration of global sensitivity and feature selection techniques. Appl. Energy, 315, Jun. 2022, 118956, 10.1016/j.apenergy.2022.118956.
Gunay, H.B., Ouf, M., Newsham, G., O'Brien, W., Sensitivity analysis and optimization of building operations. Energy Build. 199 (Sep. 2019), 164–175, 10.1016/j.enbuild.2019.06.048.
Naji, S., Aye, L., Noguchi, M., Sensitivity analysis on energy performance, thermal and visual discomfort of a prefabricated house in six climate zones in Australia. Appl. Energy, 298, Sep. 2021, 117200, 10.1016/j.apenergy.2021.117200.
Russo, E., Domeisen, D.I.V., Increasing intensity of extreme heatwaves: the crucial role of metrics. Geophys. Res. Lett., 50(14), Jul. 2023, 10.1029/2023GL103540 e2023GL103540.
Kaltsatou, A., Kenny, G.P., Flouris, A.D., The impact of heat waves on mortality among the elderly: a mini systematic review. Journal of Geriatric Medicine and Gerontology, 4(3), 2018, 53, 10.23937/2469-5858/1510053.
Crawley, D.B., et al. EnergyPlus: creating a new-generation building energy simulation program. Energy Build. 33:4 (Apr. 2001), 319–331, 10.1016/S0378-7788(00)00114-6.
Delgarm, N., Sajadi, B., Azarbad, K., Delgarm, S., Sensitivity analysis of building energy performance: a simulation-based approach using OFAT and variance-based sensitivity analysis methods. J. Build. Eng. 15 (Jan. 2018), 181–193, 10.1016/j.jobe.2017.11.020.
Ebrahimpour, A., Maerefat, M., Application of advanced glazing and overhangs in residential buildings. Energy Convers. Manag. 52:1 (Jan. 2011), 212–219, 10.1016/j.enconman.2010.06.061.
DesignBuilder, “Uncertainty and sensitivity analysis,” DesignBuilder. [Online]. Available: https://designbuilder.co.uk/helpv7.0/Content/UASA.htm. Accessed on: June 22, 2023.
McKay, M.D., Beckman, R.J., Conover, W.J., A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21(2), 1979, 239, 10.2307/1268522.
DesignBuilder. Sensitivity analysis outputs - standardised regression coefficient. [Online] https://designbuilder.co.uk/helpv7.0/Content/SensitivityOutputsSRC.htm. (Accessed 9 June 2023)
DesignBuilder. Uncertainty and sensitivity analysis - calculation options general tab. [Online] https://designbuilder.co.uk/helpv7.0/Content/UASACalculationOptionsGeneral.htm. (Accessed 12 June 2023)
Dutta, S., Gandomi, A.H., Chapter 15 - design of experiments for uncertainty quantification based on polynomial chaos expansion metamodels. Samui, P., Tien Bui, D., Chakraborty, S., Deo, R.C., (eds.) Handbook of Probabilistic Models, 2020, Butterworth-Heinemann, 369–381, 10.1016/B978-0-12-816514-0.00015-1.
Tian, W., A review of sensitivity analysis methods in building energy analysis. Renew. Sustain. Energy Rev. 20 (2013), 411–419, 10.1016/j.rser.2012.12.014.
Hopfe, C.J., Uncertainty and Sensitivity Analysis in Building Performance Simulation for Decision Support and Design Optimization., 2009, Eindhoven university of Technology, 10.6100/IR643321 PhD Thesis.
Domínguez-Muñoz, F., Cejudo-López, J.M., Carrillo-Andrés, A., Uncertainty in peak cooling load calculations. Energy Build. 42:7 (2010), 1010–1018, 10.1016/j.enbuild.2010.01.013.
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., A fast and elitist multi-objective genetic algorithm: nsga-II. IEEE Trans. Evol. Comput. 6:2 (2002), 182–197, 10.1109/4235.996017.
Abdel-Basset, M., Abdel-Fatah, L., Sangaiah, A.K., Chapter 10 - metaheuristic algorithms: a comprehensive review. Sangaiah, A.K., Sheng, M., Zhang, Z., (eds.) Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications, 2018, Academic Press, 185–231, 10.1016/B978-0-12-813314-9.00010-4.
Deb, K., Multiobjective Optimization Using Evolutionary Algorithms: an Introduction. KanGAL report No. 2011003, 2011, Indian Institute of Technology Kanpur, India [Online] https://www.egr.msu.edu/∼kdeb/papers/k2011003.pdf. (Accessed 9 May 2024)
Benincá, L., Crespo Sánchez, E., Passuello, A., Karini Leitzke, R., Grala da Cunha, E., Maria González Barroso, J., Multi-objective optimization of the solar orientation of two residential multifamily buildings in south Brazil. Energy Build., 285, Apr. 2023, 112838, 10.1016/j.enbuild.2023.112838.
DesignBuilder. Optimisation calculation options dialog - general tab.". [Online] https://designbuilder.co.uk/helpv7.0/Content/OptimisationCalculationOptionsDialogGeneral.htm. (Accessed 18 July 2023)
DesignBuilder. “Optimisation,” DesignBuilder. [Online https://designbuilder.co.uk/helpv7.0/Content/Optimisation.htm. (Accessed 9 June 2023)
House, Passive, Passive house requirements. Passive House Institute, 2015 [Online] https://passiv.de/en/02_informations/02_passive-house-requirements/02_passive-house-requirements.htm. (Accessed 24 May 2023)
CEN. EN 16798-1: Energy Performance of Buildings - Ventilation for Buildings - Indoor Environmental Input Parameters for Design and Assessment of Energy Performance of Buildings Addressing Indoor Air Quality, Thermal Environment, Lighting and Acoustics. 2019, European Committee for Standardization, Brussels, Belgium.
Safi, T.O., Évaluation de la résilience thermique et de l'efficacité énergétique dans un établissement de soins en Wallonie. 2023, University of Liege, Master Thesis [Online]. Available: http://hdl.handle.net/2268.2/17730.
CPAS, “Maison de repos et de soins “Benjamin Grugeon”,” CPAS de Tournai, Belgium. [Online]. Available at: https://www.cpas-tournai.be/aines/maison-de-repos-et-de-soins-benjamin-grugeon. Accessed on May 8, 2024.
ANSI/ASHRAE. ASHRAE Guideline 14: Measurement of Energy, Demand, and Water Savings. 2014, American Society of Heating, Refrigerating and Air Conditioning Engineers, Atlanta, GA, USA.
Civitatis Brussels, “Brussels weather. ” [Online]. Available: https://www.introducingbrussels.com/weather. (Accessed 23 June 2023)
Safi, T.O., Amaripadath, D., Rahif, R., Attia, S., Building Energy Performance Simulation Model for a Nearly Zero-Energy Nursing Home in Belgium. 2023, Harvard Dataverse, Cambridge, MA, USA, 10.7910/DVN/JSZRNT.
Albatayneh, A., Sensitivity analysis optimisation of building envelope parameters in a sub-humid Mediterranean climate zone. Energy Explor. Exploit. 39:6 (Nov. 2021), 2080–2102, 10.1177/01445987211020432.
ANSI/ASHRAE. ASHRAE Standard 55: Thermal Environmental Conditions for Human Occupancy. 2020, American Society of Heating, Refrigerating and Air Conditioning Engineers, Atlanta, GA, USA.
Kittel, C., Present and Future Sensitivity of the Antarctic Surface Mass Balance to Oceanic and Atmospheric Forcings: Insights with the Regional Climate Model MAR., 2021, University of Liege, Belgium Ph.D. Thesis https://hdl.handle.net/2268/258491.
Doutreloup, S., et al. Historical and future weather data for dynamic building simulations in Belgium using the regional climate model MAR: typical and extreme meteorological year and heatwaves. Earth Syst. Sci. Data 14 (Jul. 2022), 3039–3051, 10.5194/essd-14-3039-2022.
Crawley, D.B., Barnaby, C.S., Weather and climate in building performance simulation. Building Performance Simulation for Design and Operation, 2019, 191–220, 10.1201/9780429402296-6.
Finkelstein, J.M., Schafer, R.E., Improved goodness-of-fit tests. Biometrika 58:3 (1971), 641–645, 10.1093/biomet/58.3.641.
ISO. ISO 15927-4: Hygrothermal Performance of Buildings - Calculation and Presentation of Climatic Data - Part 4: Hourly Data for Assessing the Annual Energy Use for Heating and Cooling. 2005, International Standards Organization, Geneva, Switzerland.
Doutreloup, S., Fettweis, X., Typical & Extreme Meteorological Year and Heatwaves for Dynamic Building Simulations in Belgium Based on MAR Model Simulations. Nov. 09, 2021, Zenodo, 10.5281/zenodo.5606983.
Michelozzi, P., et al. High temperature and hospitalizations for cardiovascular and respiratory causes in 12 European cities. Am. J. Respir. Crit. Care Med. 179:5 (2009), 383–389, 10.1164/rccm.200802-217oc.
Amaripadath, D., Joshi, M.Y., Hamdy, M., Petersen, S., Stone, B. Jr., Attia, S., Thermal resilience in a renovated nearly zero-energy dwelling during intense heat waves. Journal of Building Performance Simulation, 2023, 1–20, 10.1080/19401493.2023.2253460.
Romeo, C., Zinzi, M., Impact of a cool roof application on the energy and comfort performance in an existing non-residential building. A Sicilian case study. Energy Build. 67 (2013), 647–657, 10.1016/j.enbuild.2011.07.023.
Rawat, M., Singh, R.N., A study on the comparative review of cool roof thermal performance in various regions. Energy and Built Environment 3:3 (2022), 327–347, 10.1016/j.enbenv.2021.03.001.
Bevilacqua, P., Mazzeo, D., Bruno, R., Arcuri, N., Experimental investigation of the thermal performances of an extensive green roof in the Mediterranean area. Energy Build. 122 (2016), 63–79, 10.1016/j.enbuild.2016.03.062.
Polo-Labarrios, M.A., Quezada-García, S., Sánchez-Mora, H., Escobedo-Izquierdo, M.A., Espinosa-Paredes, G., Comparison of thermal performance between green roofs and conventional roofs. Case Stud. Therm. Eng., 21, 2020, 100697, 10.1016/j.csite.2020.100697.
Runsheng, T., Etzion, Y., Erell, E., Experimental studies on a novel roof pond configuration for the cooling of buildings. Renew. Energy 28:10 (2003), 1513–1522, 10.1016/S0960-1481(03)00002-8.
Sharifi, A., Yamagata, Y., Roof ponds as passive heating and cooling systems: a systematic review. Appl. Energy 160 (2015), 336–357, 10.1016/j.apenergy.2015.09.061.
Amaripadath, D., Levinson, R., Rawal, R., Attia, S., Multi-criteria decision support framework for climate change-sensitive thermal comfort evaluation in European buildings. Energy Build., 303, 2024, 113804, 10.1016/j.enbuild.2023.113804.
Amaripadath, D., Paolini, R., Sailor, D.J., Attia, S., Comparative assessment of night ventilation performance in a nearly zero-energy office building during heat waves in Brussels. J. Build. Eng., 78, 2023, 107611, 10.1016/j.jobe.2023.107611.
AViQ, “Maison de repos,” Wallonie Familles Sante Handicap AViQ. [Online]. Available: https://www.aviq.be/fr/hebergement/aines/maison-de-repos. Accessed on: July 20, 2023.
Eurostat, “The share of elderly people continues to increase,” Eurostat Statistics Explained. [Online]. Available: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Population_structure_and_ageing#The_share_of_elderly_people_continues_to_increase. Accessed on: June 02, 2024.
Tian, Z., Zhang, X., Jin, X., Zhou, X., Si, B., Shi, X., Towards adoption of building energy simulation and optimization for passive building design: a survey and a review. Energy Build. 158 (2018), 1306–1316, 10.1016/j.enbuild.2017.11.022.
Mahar, W.A., Verbeeck, G., Reiter, S., Attia, S., Sensitivity analysis of passive design strategies for residential buildings in cold semi-arid climates. Sustainability, 12(3), Feb. 2020, 1091, 10.3390/su12031091.
Ball, M.M., et al. Quality of life in assisted living facilities: viewpoints of residents. J. Appl. Gerontol. 19:3 (2000), 304–325, 10.1177/073346480001900304.