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

Ganesh J G; JAYAKRISHNAN V M; Sreekanth S

doi:10.36948/ijfmr.2024.v06i01.11631

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

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