Prescriptive tool for zero-emissions building fenestration design using hybrid metaheuristic algorithms
Fuente:
arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866917123830317056 |
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| author | Caro, Rosana Cruz, Lorena Martinez, Arturo Naharro, Pablo S. Muelas, Santiago Sancho, Kevin King Cuerda, Elena Barbero-Barrera, Maria del Mar LaTorre, Antonio |
| author_facet | Caro, Rosana Cruz, Lorena Martinez, Arturo Naharro, Pablo S. Muelas, Santiago Sancho, Kevin King Cuerda, Elena Barbero-Barrera, Maria del Mar LaTorre, Antonio |
| contents | Designing Zero-Emissions Buildings (ZEBs) involves balancing numerous complex objectives that traditional methods struggle to address. Fenestration, encompassing façade openings and shading systems, plays a critical role in ZEB performance due to its high thermal transmittance and solar radiation admission. This paper presents a novel simulation-based optimization method for fenestration designed for practical application. It uses a hybrid metaheuristic algorithm and relies on rules and an updatable catalog, to fully automate the design process, create a highly diverse search space, minimize biases, and generate detailed solutions ready for architectural prescription. Nineteen fenestration variables, over which architects have design flexibility, were optimized to reduce heating, cooling demand, and thermal discomfort in residential buildings. The method was tested across three Spanish climate zones. Results demonstrate that the considered optimization algorithm significantly outperforms the baseline Genetic Algorithm in both quality and robustness, with these differences proven to be statistically significant. Furthermore, the findings offer valuable insights for ZEB design, highlighting challenges in reducing cooling demand in warm climates, and showcasing the superior efficiency of automated movable shading systems compared to fixed solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04102 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prescriptive tool for zero-emissions building fenestration design using hybrid metaheuristic algorithms Caro, Rosana Cruz, Lorena Martinez, Arturo Naharro, Pablo S. Muelas, Santiago Sancho, Kevin King Cuerda, Elena Barbero-Barrera, Maria del Mar LaTorre, Antonio Neural and Evolutionary Computing Designing Zero-Emissions Buildings (ZEBs) involves balancing numerous complex objectives that traditional methods struggle to address. Fenestration, encompassing façade openings and shading systems, plays a critical role in ZEB performance due to its high thermal transmittance and solar radiation admission. This paper presents a novel simulation-based optimization method for fenestration designed for practical application. It uses a hybrid metaheuristic algorithm and relies on rules and an updatable catalog, to fully automate the design process, create a highly diverse search space, minimize biases, and generate detailed solutions ready for architectural prescription. Nineteen fenestration variables, over which architects have design flexibility, were optimized to reduce heating, cooling demand, and thermal discomfort in residential buildings. The method was tested across three Spanish climate zones. Results demonstrate that the considered optimization algorithm significantly outperforms the baseline Genetic Algorithm in both quality and robustness, with these differences proven to be statistically significant. Furthermore, the findings offer valuable insights for ZEB design, highlighting challenges in reducing cooling demand in warm climates, and showcasing the superior efficiency of automated movable shading systems compared to fixed solutions. |
| title | Prescriptive tool for zero-emissions building fenestration design using hybrid metaheuristic algorithms |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2512.04102 |