Computational Optimization of Phase Change Material Integration Using  Machine Learning for Enhanced Building Heating Efficiency in Cold  Climates

Dr. Brajesh Kumar

Computational Optimization of Phase Change Material Integration Using Machine Learning for Enhanced Building Heating Efficiency in Cold Climates

Authors

Dr. Brajesh Kumar

Keywords:

Abstract

Phase Change Materials (PCMs) represent a transformative approach to improve the energyefficiency and thermal comfort of buildings during winter heating periods. While PCMs offersubstantial latent heat storage capacity

References

Alvarez, S. et al. (2019). “Experimental Study of PCM Wallboards for Thermal Energy Storage in Buildings,” Energy Procedia, 158, pp. 379–384.

Banerjee, T., & Biswas, D. (2021). “Surrogate-Assisted Mult objective Optimization of Phase Change Materials,” Applied Energy, 282, 116098

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Published

2024-02-20

How to Cite

Dr. Brajesh Kumar. (2024). Computational Optimization of Phase Change Material Integration Using Machine Learning for Enhanced Building Heating Efficiency in Cold Climates . Journal of Computational Analysis and Applications (JoCAAA), 32(2), 520–530. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3460