Abstract
The use of PCMs to reduce the rate of heat exchange, as a technique to boost the thermal resistance of building walls, has been considered by many researchers. The performance of these materials depends on their properties, the temperature profile of the building and finally their installation location. In this study, considering these three variables as input, their effects on output variables, which include cooling/heating loads and annual energy consumption, were examined. The neural network was used to trend the correlation between input and output variables and 240 numerical solutions were used to train it. The results showed that the installation location of PCM is highly dependent on the thermal insulation installation location. It was found that in most cases, if the PCM is installed exactly next to the thermal insulation (L3 position), the cooling load will be significantly reduced. In most cases, the PCM installation location should be close to the indoor rather than outdoor space. The genetic algorithm showed that to reduce annual energy consumption, the internal temperature should be set at 24.19 °C, and in addition, it is recommended to use PCM 22 in the L3 position. Under these conditions, CO 2 emissions declined by 23.6%.
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