
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
Home
Research Paper
Submit Research Paper
Publication Guidelines
Publication Charges
Upload Documents
Track Status / Pay Fees / Download Publication Certi.
Editors & Reviewers
View All
Join as a Reviewer
Get Membership Certificate
Current Issue
Publication Archive
Conference
Publishing Conf. with IJFMR
Upcoming Conference(s) ↓
WSMCDD-2025
GSMCDD-2025
Conferences Published ↓
RBS:RH-COVID-19 (2023)
ICMRS'23
PIPRDA-2023
Contact Us
Plagiarism is checked by the leading plagiarism checker
Call for Paper
Volume 7 Issue 2
March-April 2025
Indexing Partners



















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 |
DOI | https://doi.org/10.36948/ijfmr.2024.v06i01.11631 |
Short DOI | https://doi.org/gtdsb6 |
Share this

E-ISSN 2582-2160

CrossRef DOI is assigned to each research paper published in our journal.
IJFMR DOI prefix is
10.36948/ijfmr
Downloads
All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.
