Cluster analysis applied to residential thermal performance study

Authors

DOI:

https://doi.org/10.46421/entac.v19i1.2143

Keywords:

Computational simulation, Thermal performance, Cluster analysis

Abstract

Building thermal performance analysis may involve simulation of several cases with constructive systems within different composition, but similar performance. Thus, it is not necessary simulation of all constructive systems, but just the representative cases. This study uses a k-medoids clustering method to determine representative roofs and representative external walls from a data base. The results show a similar thermal performance for cases in the same cluster. The variation between the representative case and other cases was less than 5% for most clusters.

Author Biographies

Letícia Gabriela Eli, Universidade Federal de Santa Catarina - LabEEE

Mestrado em Engenharia Civil pela Universidade Federal de Santa Catarina. Doutoranda em Engenharia Civil pela Universidade Federal de Santa Catarina.

Amanda Fraga Krelling, Universidade Federal de Santa Catarina

Mestrado em Engenharia Civil pela Universidade Federal de Santa Catarina. Doutoranda em Engenharia Civil na Universidade Federal de Santa Catarina.

Vanessa Aparecida Caieiro da Costa, Saint-Gobain Researcher Brasil

Mestrado em Arquitetura e Urbanismo pela Universidade de São Paulo. Doutoranda em Arquitetura e Urbanismo pela Universidade de São Paulo. Pesquisadora na Saint-Gobain Researcher Brasil.

Ana Paula Melo, Universidade Federal de Santa Catarina

Doutora em Engenharia Civil pela Universidade Federal de Santa Catarina. Professora do Departamento de Engenharia Civil da Universidade Federal de Santa Catarina. 

Roberto Lamberts, Universidade Federal de Santa Catarina

Doutor em Engenharia Civil pela University of Leeds. Professor do Departamento de Engenharia Civil da Universidade Federal de Santa Catarina. 

References

ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS. NBR 15220-3: Desempenho térmico de edificações parte 3: Zoneamento bioclimático brasileiro e diretrizes construtivas para habitações unifamiliares de interesse social. Rio de Janeiro, 2005.

ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS. NBR 15575-1-1:2021 Errata 1:2021: Edificações habitacionais - Desempenho Parte 1-1: Base-padrão de arquivos climáticos para a avaliação do desempenho térmico por meio do procedimento de simulação computacional. Rio de Janeiro, 2021.

ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS. NBR 15575-1: Emenda de Desempenho Térmico da norma de Edificações Habitacionais – Desempenho (ABNT NBR 15575- Parte 1). Rio de Janeiro, 2020.

BIENVENIDO-HUERTAS, D.; SÁNCHEZ-GARCIA, D.; RUBIO-BELLIDO, C.; PULIDO-ARCAS, J. A. Analysing the inequitable energy framework for the implementation of nearly zero energy buildings (nZEB) in Spain. Journal of Building Engineering. v. 35, 102011, nov. 2020. DOI: https://doi.org/10.1016/J.JOBE.2020.102011

BORGER, P.; TRAVESSET-BARO, O.; PAGES-RAMON, A. Hybrid approach to representative building archetypes development for urban models – A case study in Andorra. Building and Environment. v. 215, 108958, mar. 2022. DOI: https://doi.org/10.1016/J.BUILDENV.2022.108958

CRAN – R. The R Project for Statistical Computing (2021). Disponível em: https://www.r-project.org/. Acesso em: 27 maio 2022.

DOE, United States Department of Energy. EnergyPlus 9.0. Disponível em: https://github.com/NREL/EnergyPlus/releases/tag/v9.0.1. Acesso em: 27 maio 2022.

ELI, L. G.; KRELLING, A. F.; OLINGER, M. S.; MELO, A. P; LAMBERTS, R. Thermal performance of residential building with mixed-mode and passive cooling strategies: The Brazilian context. Energy and Buildings. v. 244, Special Issue 111047, abr. 2021. DOI: https://doi.org/10.1016/j.enbuild.2021.111047

HAIR, J. F.; BLACK, W. C.; BABIN, B. J.; ANDERSON, R. E. Multivariate Data Analysis. 7. ed. London: Prentice Hall, 2010.

JAIN, A. K.; MURTY, M. N.; FLYNN, P. J. Data clustering: a review. ACM Computing Surveys. v. 31, n 3, p. 264-323, set. 1999. DOI: https://doi.org/ 10.1145/331499.331504

JOTA, P. R. S.; SILVA, V. R. B.; JOTA, F. G. Building load management using cluster and statistical analyses. International Journal of Electrical Power & Energy Systems. v. 33, 1498-1505, jul. 2011. DOI: https://doi.org/10.1016/J.IJEPES.2011.06.034

MAECHLER, M.; ROUSSEEUW, P.; STRUYF, A.; HUBERT, M.; HORNIK, K.; STUDER, M.; ROUDIER, P.; GONZALEZ, J.; KOZLOWSKI, K.; SCHUBERT, E. Package Cluster. CRAN. Disponível em: https://cran.r-project.org/web/packages/cluster/cluster.pdf. Acesso em: 27 maio 2022.

MARSHALL, E.; STEINBERGER, J. K.; DUPONT, V.; FOXON, T. J. Combining energy efficiency measure approaches and occupancy patterns in building modelling in the UK residential context. Energy and Buildings. v. 111, p. 98-108, nov. 2015. DOI: https://doi.org/10.1016/j.enbuild.2015.11.039

MAZZAFERRO, L.; MACHADO, R. M. S.; MELO, A. P; LAMBERTS, R. Do we need building performance data to propose a climatic zoning for building energy efficiency regulations? Energy and Buildings. v. 225, 110303, jul. 2020. DOI: https://doi.org/10.1016/J.ENBUILD.2020.110303

MIRKES, E. M. K-means and K-medoids applet. University of Leicester. Disponível em: http://www.math.le.ac.uk/people/ag153/homepage/KmeansKmedoids/Kmeans_Kmedoids.html. Acesso em: 27 maio 2022.

SAXENA, A.; PRASAD, M.; GUPTA, A.; BHARILL, N.; PATEL, O. P.; TIWARI, A.; ER, M. J.; DING, W.; LIN, C. A review of clustering techniques and developments. Neurocomputing. v. 267, 664-681, jul. 2017. DOI: https://doi.org/10.1016/J.NEUCOM.2017.06.053

SORGATO, M. J.; MELO, A. P; LAMBERTS, R. The effect of window opening ventilation control on residential building energy consumption. Energy and Buildings. v. 133, p. 1-13, set. 2016. DOI: https://doi.org/10.1016/j.enbuild.2016.09.059

TRIANA, M. A.; LAMBERTS; R., SASSI, P. Characterisation of representative building typologies for social housing projects in Brazil and its energy performance. Energy Policy. v. 87, p. 524-541, aug. 2015. DOI: https://doi.org/10.1016/j.enpol.2015.08.041

TRIANA, M. A.; LAMBERTS; R., SASSI, P. Should we consider climate change for Brazilian social housing? Assessment of energy efficiency adaptation measures. Energy and Buildings. v. 158, p. 1379-1392, nov. 2018. DOI: https://doi.org/10.1016/j.enbuild.2017.11.003

WALSH, A.; CÓSTOLA, D.; LABAKI, L. C. Review of methods for climatic zoning for building energy efficiency programs. Building and Environment. v. 112, 337-350, nov. 2016. DOI: https://doi.org/10.1016/J.BUILDENV.2016.11.046

Published

2022-11-07

How to Cite

ELI, Letícia Gabriela; KRELLING, Amanda Fraga; COSTA, Vanessa Aparecida Caieiro da; MELO, Ana Paula; LAMBERTS, Roberto. Cluster analysis applied to residential thermal performance study. In: NATIONAL MEETING OF BUILT ENVIRONMENT TECHNOLOGY, 19., 2022. Anais [...]. Porto Alegre: ANTAC, 2022. p. 1–10. DOI: 10.46421/entac.v19i1.2143. Disponível em: https://eventos.antac.org.br/index.php/entac/article/view/2143. Acesso em: 3 nov. 2024.

Issue

Section

(Inativa) Conforto Ambiental e Eficiência Energética

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