Archivos climáticos típicos y extremos para la evaluación del desempeño de edificios en Brasil

Autores/as

DOI:

https://doi.org/10.46421/encacelacac.v18i1.7183

Palabras clave:

Datos meteorológicos, Simulación del rendimiento del edificio, Edificios residenciales, Análisis paramétrico

Resumen

La simulación del rendimiento de un edificio depende en gran medida de las condiciones climáticas proporcionadas por el simulador. Normalmente, se prefieren las condiciones climáticas típicas para las simulaciones del rendimiento de los edificios, pero el escenario climático actual muestra que estas condiciones pueden no ser suficientes. Este estudio se centró en comparar condiciones típicas y extremas y analizó sus impactos en la temperatura de funcionamiento y el uso de energía de una casa unifamiliar. La evaluación climática inicial muestra un aumento en los grados-hora de enfriamiento y la irradiación normal directa. Los resultados de la simulación mostraron que los años extremos tuvieron una temperatura operativa promedio 1,3 °C más alta que las condiciones típicas, además de un aumento del 20% en el uso de energía.

Biografía del autor/a

Mario Alves da Silva, Free University of Bozen-Bolzano

Doctor in Architecture and Urbanism at the Federal University of Viçosa. Research Assistant at the Free University of Bozen-Bolzano (Bolzano, Italy).

Giovanni Pernigotto, Free University of Bozen-Bolzano

PhD in Industrial Engineering at the University of Padova. Associate Professor at the Free University of Bozen-Bolzano (Bolzano, Italy).

Prada Prada, University of Trento

PhD in Environmental Engineering at the University of Trento. Associate Professor at the University of Trento (Trento, Italy).

Andrea Gasparella, Free University of Bozen-Bolzano

PhD in Energetics at the University of Padova. Full Professor at the Free University of Bozen-Bolzano (Bolzano, Italy).

Joyce Correna Carlo, Universidade Federal de Viçosa

Doctor in Civil Engineering at the Federal University of Santa Catarina. Associate Professor at the Federal University of Viçosa (Viçosa - MG, Brazil).

Citas

ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS - ABNT. NBR 15575: Edificações habitacionais - Desempenho. Rio de Janeiro, 2021.

BRACHT, M. K. et al. Multiple regional climate model projections to assess building thermal performance in Brazil: Understanding the uncertainty. Journal of Building Engineering, v. 88, p. 109248, 2024. ISSN: 2352-7102, DOI: https://doi.org/10.1016/j.jobe.2024.109248.

CALVIN, K. et al. IPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland, 2023. DOI: 10.59327/IPCC/AR6-9789291691647.

CRAWLEY, D. B.; HUANG, Y. J.; BERKELEY, L. Does It Matter Which Weather Data You Use in Energy Simulations? Building Energy Simulation User News, v. 18, no 1, p. 25–31, 1997.

DA SILVA, M. A. et al. Impact of the Type of Weather Files on the Outcome of a Weather-Based Climate Classification: The Case of Brazil. Em: BERARDI, U. (Org.). Multiphysics and Multiscale Building Physics. IABP 2024. Lecture Notes in Civil Engineering. Singapore: Springer, 2025. v. 553, p. 258–263. DOI: 10.1007/978-981-97-8309-0_34.

HALL, I. J. et al. Generation of a typical meteorological year. United States, 1978. Disponível em: <https://www.osti.gov/biblio/7013202>.

HOSSEINI, M.; BIGTASHI, A.; LEE, B. A systematic approach in constructing typical meteorological year weather files using machine learning. Energy and Buildings, v. 226, 2020. ISSN: 03787788, DOI: 10.1016/j.enbuild.2020.110375.

MACHARD, A. et al. Typical and extreme weather datasets for studying the resilience of buildings to climate change and heatwaves. Scientific Data, v. 11, no 1, p. 531, 2024. ISSN: 2052-4463, DOI: 10.1038/s41597-024-03319-8.

MUÑOZ-SABATER, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth System Science Data, v. 13, no 9, p. 4349–4383, 2021. DOI: 10.5194/essd-13-4349-2021.

PAPAKYRIAKOU, A.; BIGTASHI, A.; LEE, B. Evaluating the applicability of a machine learning methodology to improve TMY weather file generation for different Canadian climate zones. Journal of Building Engineering, v. 95, p. 110096, 2024. ISSN: 2352-7102, DOI: 10.1016/J.JOBE.2024.110096.

PERNIGOTTO, G. et al. Analysis and improvement of the representativeness of EN ISO 15927-4 reference years for building energy simulation. Journal of Building Performance Simulation, v. 7, no 6, p. 391–410, 2014. ISSN: 19401493, DOI: 10.1080/19401493.2013.853840.

PERNIGOTTO, G.; PRADA, A.; GASPARELLA, A. Extreme reference years for building energy performance simulation. Journal of Building Performance Simulation, v. 13, no 2, p. 152–166, 2020. ISSN: 19401507, DOI: 10.1080/19401493.2019.1585477.

RODRIGUES, E.; PARENTE, J.; FERNANDES, M. S. Building for tomorrow: Analyzing ideal thermal transmittances in the face of climate change in Brazil. Applied Energy, v. 355, p. 122360, 2024. ISSN: 0306-2619, DOI: https://doi.org/10.1016/j.apenergy.2023.122360.

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, 2015. ISSN: 0301-4215, DOI: https://doi.org/10.1016/j.enpol.2015.08.041.

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Publicado

2025-08-16

Cómo citar

ALVES DA SILVA, Mario; PERNIGOTTO, Giovanni; PRADA, Prada; GASPARELLA, Andrea; CARLO, Joyce Correna. Archivos climáticos típicos y extremos para la evaluación del desempeño de edificios en Brasil. In: ENCONTRO NACIONAL DE CONFORTO NO AMBIENTE CONSTRUÍDO, 18., 2025. Anais [...]. [S. l.], 2025. DOI: 10.46421/encacelacac.v18i1.7183. Disponível em: https://eventos.antac.org.br/index.php/encac/article/view/7183. Acesso em: 3 may. 2026.

Número

Sección

5. Eficiência Energética