Energy Optimization with Bioinspired Geometric Patterns: Artificial Intelligence in Passive Architectural Design
Optimización Energética con Patrones Geométricos Bioinspirados: Inteligencia Artificial en el Diseño Pasivo Arquitectónico
timização Energética com Padrões Geométricos Bioinspirados: Inteligência Artificial no Design Passivo Arquitetônico
| 2025 | |
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Biomimicry Artificial Intelligence Passive Design Sustainable Architecture Parametric Models Energy Efficiency Computational Simulations Solar Protection Bioinspired Geometries Architectural Optimization Biomímesis Inteligencia Artificial Diseño Pasivo Arquitectura Sostenible Modelos Paramétricos Eficiencia Energética Simulaciones Computacionales Protección Solar Geometrías Bioinspiradas Optimización Arquitectónica Biomimética Inteligência Artificial Design Passivo Arquitetura Sustentável Modelos Paramétricos Eficiência Energética Simulações Computacionais Proteção Solar Geometrias Bioinspiradas Otimização Arquitetônica |
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| Español | |
| Universidad ORT Uruguay | |
| RAD | |
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https://revistas.ort.edu.uy/anales-de-investigacion-en-arquitectura/article/view/4048
http://hdl.handle.net/20.500.11968/7606 |
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| Acceso abierto |
| Sumario: | Climate change, together with the sustained rise in energy consumption across the building sector, poses critical challenges for architectural sustainability—particularly in Mediterranean climates, where summer solar radiation is especially intense. This study evaluates the hypothesis that biomimicry, supported by parametric tools and artificial intelligence (AI), can overcome the limitations of conventional passive design by enhancing both the energy efficiency and climatic responsiveness of building envelopes. The purpose of the research is to verify this hypothesis through the examination of three bio-inspired geometric configurations—Delaunay, Voronoi, and Metaball. These models were developed using biomimetic principles, digital simulations, and AI algorithms, and were applied to a representative architectural prism located in the Community of Madrid to assess their capacity to reduce incident solar radiation during the summer months. Pattern generation was carried out in Rhinoceros 8/Grasshopper, while climatic performance was assessed with Ladybug Tools 1.5.0 (Radiance/Daysim). The geometric variants were optimized with the multi-objective genetic algorithm NSGA-II, implemented in Python 3.10 with the DEAP library and integrated into Grasshopper via GhPython. The specific objectives were (i) to decrease incident solar radiation and (ii) to limit material complexity, thereby ensuring the constructability of the proposals. The results indicate that this approach not only achieves significant mitigation of solar radiation but also delivers innovative and adaptable solutions that integrate functionality, energy efficiency, and aesthetics. These strategies hold considerable potential to redefine sustainable architectural design, laying the groundwork for new practical applications that reduce energy consumption and address contemporary climatic challenges. The study underscores the impact of the convergence of biomimicry, parametric technology, and artificial intelligence, opening pathways for the development of more resilient architectural models aligned with global energy demands. |
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