Download pdf
Energy Transmission Optimization in Multilayer Glass Models Using Particle Swarm Optimization
- 1 New Energy and Intelligent Connected Vehicle Academy, University of Sanya, Sanya, China
* Author to whom correspondence should be addressed.
Abstract
The intensification of global climate change and rising temperatures have underscored the need for energy-efficient building designs, particularly in southern China. This study proposes a particle swarm optimization (PSO) approach to optimize the thickness of triple-glazed windows (L1, L2, L3) to minimize sunlight transmission energy within the wavelength range of 300-2000 nm. A multilayer glass model was constructed, accounting for light transmission, reflection, and absorption properties. By simulating various thickness combinations, the transmitted energy was calculated across the wavelength spectrum. The PSO algorithm was then employed to search for the optimal thickness configuration that minimizes the total transmitted sunlight energy. Experimental results indicate that the proposed method significantly reduces indoor sunlight transmission compared to traditional design methods, enhancing both energy efficiency and occupant comfort. The findings highlight the importance of optimizing glass thickness and material selection in window design, balancing light transmission and shading to suit specific architectural needs. Additionally, this study underscores the impact of PSO parameters on convergence and solution diversity, suggesting further refinements for practical applications. The approach provides a scientific basis for building energy-saving designs and can be adapted to various climatic conditions and material specifications. Future work should focus on incorporating weather variations and expanding model complexity to enhance the algorithm's applicability in real-world scenarios.
Keywords
Particle swarm optimization, sunlight transmission, energy efficiency in buildings.
[1]. Mahmoud, A. H. K., de Rossi, S., Meltchakov, E., Capitanio, B., Thomasset, M., Vallet, M., & Delmotte, F. (2023). Experimental study and modeling of extreme ultraviolet 4000 lines/mm diffraction gratings coated with periodic and aperiodic Al/Mo/SiC multilayers. Applied optics, 63(1), 30-41.
[2]. Qi, R., Wu, J., Yu, J., He, C., Jiang, L., Yu, Y., ... & Wang, Z. (2023). Narrowband EUV Sc/Si Multilayer for the Solar Upper Transition Region Imager at 46.5 nm. Research in Astronomy and Astrophysics, 23(10), 105002.
[3]. Zhang, Y., Zhao, H., Zhu, X., Zhao, Z., & Zuo, J. (2019). Strain Measurement Quantization Technology based on DAS System. In Proceedings of the 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) (pp. 214-218). IEEE.
[4]. Zhao, Z., Peng, Y., Zhu, X., Wei, X., Wang, X., & Zuo, J. (2020). Research on Prediction of Electricity Consumption in Smart Parks Based on Multiple Linear Regression. In Proceedings of the 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) (pp. 812-816). IEEE.
[5]. Xu, H., Zhu, X., Zhao, Z., Wei, X., Wang, X., & Zuo, J. (2020). Research of Pipeline Leak Detection Technology and Application Prospect of Petrochemical Wharf. In Proceedings of the 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) (pp. 263-271). IEEE.
[6]. Zhu, X., Zhao, Z., Wei, X., Wang, X., & Zuo, J. (2021). Action Recognition Method Based on Wavelet Transform and Neural Network in Wireless Network. In Proceedings of the 2021 5th International Conference on Digital Signal Processing (pp. 60-65). IEEE.
[7]. Askari, E., Flores, P., & Silva, F. (2018). A particle swarm-based algorithm for optimization of multi-layered and graded dental ceramics. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 7074-7082).
[8]. Jiao, J., Ghoreishi, S.-M., Moradi, Z., & Oslub, K. (2021). Coupled particle swarm optimization method with genetic algorithm for the static–dynamic performance of the magneto-electro-elastic nanosystem. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 7074-7082).
[9]. Karami, M. R., Jaleh, B., Eslamipanah, M., Nasri, A., & Rhee, K. Y. (2023). Design and optimization of a TiO2/RGO-supported epoxy multilayer microwave absorber by the modified local best particle swarm optimization algorithm. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 7074-7082).
[10]. Wang, R., Zhu, J., Wang, S., Wang, T., Huang, J., & Zhu, X. (2024). Multi-modal Emotion Recognition Using Tensor Decomposition Fusion and Self-supervised Multi-tasking. International Journal of Multimedia Information Retrieval, 13(4), 39.
Cite this article
Cui,Y. (2024). Energy Transmission Optimization in Multilayer Glass Models Using Particle Swarm Optimization. Applied and Computational Engineering,110,1-5.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of CONF-MLA 2024 Workshop: Securing the Future: Empowering Cyber Defense with Machine Learning and Deep Learning
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).