Rami Mohamed Mohamed Mohamed Saleh
Asst. Lecturer
Experimental Study Supported by ANN of New Fabricated Wall integrated with PCMs for Energy Saving and Temperature Uniformity of Air-Conditioned Buildings
Results in Engineering • 2026
Publication Information
Authors
RM Saleh, MA Said, WG Alshaer, Sameh A Nada
Keywords
Prefabricated wallsExtruded polystyrene XPSPCM cavityArtificial Neural Network (ANN)release timeLiquid fractionEnergy savingwalls configuration
Journal
Results in Engineering
Publisher
Elsevier
Volume
Volume 28
Issue
December 2025
Pages
108239
publication.type
International
Paper Link
Open Link
Supplementary Materials
Not Available
Abstract
The building of walls comprises numerous layers to protect against external environmental variations. Increasing
layers through the integration of PCM into the wall as a separate layer improves daily temperature regulation
inside buildings but reduces floor area, resulting in no economic justification. This study establishes a design
approach for PCM walls by creating two wall configurations. Then, experimental testing and predictive modeling
were conducted using Artificial Neural Network (ANN) trained by Feed Forward Back Propagation (FFBP) al-gorithm to assess thermal performance under different environmental conditions and various locations within
the prefabricated wall. The thermal behavior of the new fabricated walls is tested and evaluated using external
surface temperature, internal surface temperature, and average daily heat fluxes, in contrast to a reference wall.
The results show that the configuration (2) is the best configuration for reducing outer surface temperature by 3◦ C and reducing inner surface temperature by 0.53 ◦C at various outdoor temperatures. The location of PCM in
prefabricated wall near to the outside wall is the optimum location in the summer season rather than the inside
wall. The average daily energy saving decreases gradually with increasing the ambient temperature. in config-uration (2), the energy saving is about 20 % higher than in configuration (1) at different ambient temperatures.
Also, the artificial neural network model demonstrates high accuracy in predicting temperature profiles and
energy savings without including ambient temperature in the training data set. It turns out that the predictive
model agrees with the experimental tests, with the minimum and maximum relative errors for indoor and
outdoor temperatures being 0.5 to 2 % and 2 % to 4 %, respectively.
layers through the integration of PCM into the wall as a separate layer improves daily temperature regulation
inside buildings but reduces floor area, resulting in no economic justification. This study establishes a design
approach for PCM walls by creating two wall configurations. Then, experimental testing and predictive modeling
were conducted using Artificial Neural Network (ANN) trained by Feed Forward Back Propagation (FFBP) al-gorithm to assess thermal performance under different environmental conditions and various locations within
the prefabricated wall. The thermal behavior of the new fabricated walls is tested and evaluated using external
surface temperature, internal surface temperature, and average daily heat fluxes, in contrast to a reference wall.
The results show that the configuration (2) is the best configuration for reducing outer surface temperature by 3◦ C and reducing inner surface temperature by 0.53 ◦C at various outdoor temperatures. The location of PCM in
prefabricated wall near to the outside wall is the optimum location in the summer season rather than the inside
wall. The average daily energy saving decreases gradually with increasing the ambient temperature. in config-uration (2), the energy saving is about 20 % higher than in configuration (1) at different ambient temperatures.
Also, the artificial neural network model demonstrates high accuracy in predicting temperature profiles and
energy savings without including ambient temperature in the training data set. It turns out that the predictive
model agrees with the experimental tests, with the minimum and maximum relative errors for indoor and
outdoor temperatures being 0.5 to 2 % and 2 % to 4 %, respectively.