Multi-objective optimization of cooling and heating loads in residential buildings integrated with phase change materials using the artificial neural network and genetic algorithm

In this paper, a multi-objective optimization was performed to achieve the minimum cooling and heating loads in a residential building integrated with phase change material (PCM). The methods applied to fulfill this objective were numerical modeling by EnergyPlus, Grouped Method of Data Handling (GMDH) type of Artificial Neural Network (ANN) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II). In this study, design variables were melting temperature and thickness of PCM, thermal resistance of exterior walls, internal gain and infiltration rate. Additionally, objective functions included annual cooling and heating loads of the building that should be minimized. Therefore, first, EnergyPlus software was used to calculate the values of objective functions and to train the neural network. Afterward, the GMDH-type neural network was applied to derive polynomials computing the objective functions from input variables. Then, Pareto optimal points for objective functions were obtained through using these polynomials and NSGA-II multi-objective optimization. Finally, the optimum point was determined by a financial analysis. According to the obtained results, the thickness and melting temperature of the optimum PCM layer in the residential building in Tehran were equal to 0.032 m and 24.58 degrees C, respectively.