International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 2 March-April 2024 Submit your research before last 3 days of April to publish your research paper in the issue of March-April.

Development of Deep Learning-based Models for Predicting the Thermal Performance of Phase Change Materials

Author(s) Ganesh J G, JAYAKRISHNAN V M, Sreekanth S
Country India
Abstract Evaluating parameters such as time delay and damping coefficient in different climates and locations to evaluate Building skins combined with PCM are difficult and time-consuming to reduce heat gain. This research aims to develop a novel deep learning-based model for predicting PCM integrated roof buildings' thermal performance. When making predictions about performance, we recommend using the MKR indicator. Taking into account changes in PCM's thermophysical properties, we investigate the application of deep learning methods to predict the thermal performance of a PCM roof. Create an informative focus that includes mathematical representation considering the versatility of PCMs' thermo-physical properties. The MKR index is predicted using ANN, a deep learning technique. The results can indicate that ANN is the most effective model. During Sensitivity testing, training and analysis, independent datasets show the effectiveness and better performance of models based on artificial neural networks.
Keywords PCM integrated Roof, MKR index, Deep learning, Performance prediction, Artificial Neural Network
Field Engineering
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-01-09
Cite This Development of Deep Learning-based Models for Predicting the Thermal Performance of Phase Change Materials - Ganesh J G, JAYAKRISHNAN V M, Sreekanth S - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.11631
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.11631
Short DOI https://doi.org/gtdsb6

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