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Thermal performance analysis of PCM incorporated roof slab infrastructures using deep learning algorithms

Author

Listed:
  • Jaspal Singh
  • R.K. Tomar
  • N.D. Kaushika
  • Gopal Nandan

Abstract

PCM technology uses thermal energy storage (TES) to lessen the effects of changes in the outside temperature. PCM thermal energy storage may reduce ambient temperature changes (TES). Latent heat storage (LHS) reduces HVAC needs and enhances indoor comfort in conventional buildings. In buildings without HVAC, latent heat storage (LHS) enhances indoor thermal comfort by reducing the demand for HVAC. This study examines and measures the advantages of using PCM for building envelopes. In order to generalize the findings, a 1 m * 1 m * 1 m reference model is employed with four Indian towns located in various climate zones. Decision tree monitors temperature over time. Root mean square transforms actual and anticipated values, while mRMR selects features. Thermal testing equipment, a PCM wallboard heat storage experiment, and investigations on 5 mm, 10 mm, and 20 mm PCM plasterboard with a 220 C melting temperature are constructed to validate the results. PCM thickness reduced energy use logarithmically in all climatic zones, with temperate office buildings benefiting most.

Suggested Citation

  • Jaspal Singh & R.K. Tomar & N.D. Kaushika & Gopal Nandan, 2024. "Thermal performance analysis of PCM incorporated roof slab infrastructures using deep learning algorithms," International Journal of Critical Infrastructures, Inderscience Enterprises Ltd, vol. 20(4), pages 372-389.
    • Handle: RePEc:ids:ijcist:v:20:y:2024:i:4:p:372-389
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