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Optimization of Triple-Layer Glass Thickness for Maximizing Sunlight Incidence in Northern Winter Using Ant Colony Algorithm
- 1 University of Illinois Urbana-Champaign, Champaign, The United States
* Author to whom correspondence should be addressed.
Abstract
This study investigates the optimization of multilayer glass thickness to maximize solar heat gain in buildings located in cold northern climates, utilizing the Ant Colony Optimization (ACO) algorithm. The research focuses on enhancing thermal comfort and reducing energy consumption by optimizing the thickness of three glass layers, thereby improving solar energy transmission into indoor spaces. ACO, a metaheuristic inspired by the foraging behavior of ants, is employed due to its robustness in handling complex optimization problems. The study incorporates wavelength-specific considerations into the ACO framework to achieve a sophisticated optimization approach, resulting in improved solar energy transmission. The experimental results demonstrate that the optimized glass configuration significantly increases sunlight transmittance, especially within the target wavelength range, contributing to more energy-efficient and comfortable architectural designs. This research highlights the potential of ACO in optimizing building materials and suggests further refinement of the algorithm for enhanced performance in real-world applications.
Keywords
Ant Colony Optimization, Multi-layer Glass, Thermal Comfort.
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Cite this article
Ying,W. (2024). Optimization of Triple-Layer Glass Thickness for Maximizing Sunlight Incidence in Northern Winter Using Ant Colony Algorithm. Applied and Computational Engineering,94,71-76.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of CONF-MLA 2024 Workshop: Securing the Future: Empowering Cyber Defense with Machine Learning and Deep Learning
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