Abstract
The building walls are composed of several layers to protect the building from temperature fluctuations of the external environment. Increasing the number of layers in a building certainly strengthens the wall's ability to reduce heat transfer. But on the other hand, it increases the thickness of the walls and thus reduces the useful floor area of the building, which has no economic reason. In this study, the effect of reinforcing building walls against heat transfer using PCM was discussed. At constant PCM thickness, the installation location of this material varies from the outermost to the innermost. At the best location, the installation of PCM inside the wall/roof resulted in energy savings by 14.9 kWhmwall2 19.6 kWhmroof2 and taking into account their area, for the whole building, energy-saving was 15.9 kWhmroof2+mwall2. The effectiveness of ANN on PCM applications was amazing. Annual energy consumption for the building is 64.236 kWhm2. The neural network, by establishing a connection between the data predicts the annual energy consumption of 64.209 kWhm2. Therefore, the error was less than 0.04%. The monthly analysis also shows that for monthly EC, the error was less than 1.5%.
•The effects of reinforcing building walls against heat transfer using PCM were discussed.•The installation location of this material varies from the outermost to the innermost.•Using PCM inside the wall/roof resulted in energy savings by 14.9kWhmwall219.6kWhmroof2•For the whole building, energy-saving was 15.9kWhmroof2+mwall2owing to PCM installation in building.