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<p>This study assessed a residential prototype located in the Global South, enhanced with thermal insulation and phase change material (PCM) in the envelope. The goal was to minimize discomfort hours and cooling energy demand while addressing the impact of climate change through future predicted weather files. To achieve this, EnergyPlus, coupled with the jEPlus + EA tool, employed the Non-dominated Sorting Genetic Algorithm II (NSGAII) for multi-objective optimization, while the CCWorldWeatherGen tool was used to generate future predicted weather files. The optimization centered on a case study located in the three hottest Brazilian bioclimatic zones. Various passive parameters related to the envelope were optimized, including building orientation, glazing solution, window size, shading device depth, thermal insulation, and PCM thickness, along with PCM melting temperature. Results highlighted the importance of optimizing the building’s orientation, glazing properties, window size, and shading devices for enhancing thermal-energy performance. As for the future weather conditions, combining these strategies with thermal insulation in cooling-dominant regions reduced the discomfort hours and energy demand by up to 80% and 60%, respectively. Although the findings were based on the Brazilian context, they are applicable to similar climates, especially in Global South countries.</p>

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Exploratory analysis of PCM and thermal insulation strategies for buildings under future weather scenarios: Optimization of a residential prototype in the Global South – A case study in Brazil

  • 期刊论文

  • 发表日期：

    2025年10月21日

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  • Alexandre Santana Cruz,
  • Leopoldo Eurico Gonçalves Bastos,
  • Marcos Martinez Silvoso,
  • Thiago Melo Grabois,
  • Lucas Rosse Caldas

摘要

This study assessed a residential prototype located in the Global South, enhanced with thermal insulation and phase change material (PCM) in the envelope. The goal was to minimize discomfort hours and cooling energy demand while addressing the impact of climate change through future predicted weather files. To achieve this, EnergyPlus, coupled with the jEPlus + EA tool, employed the Non-dominated Sorting Genetic Algorithm II (NSGAII) for multi-objective optimization, while the CCWorldWeatherGen tool was used to generate future predicted weather files. The optimization centered on a case study located in the three hottest Brazilian bioclimatic zones. Various passive parameters related to the envelope were optimized, including building orientation, glazing solution, window size, shading device depth, thermal insulation, and PCM thickness, along with PCM melting temperature. Results highlighted the importance of optimizing the building’s orientation, glazing properties, window size, and shading devices for enhancing thermal-energy performance. As for the future weather conditions, combining these strategies with thermal insulation in cooling-dominant regions reduced the discomfort hours and energy demand by up to 80% and 60%, respectively. Although the findings were based on the Brazilian context, they are applicable to similar climates, especially in Global South countries.