Design Optimization Considering Variable Thermal Mass, Insulation, Absorptance of Solar Radiation, and Glazing Ratio Using a Prediction Model and Genetic Algorithm

This paper presents the optimization of building envelope design to minimize thermal load and improve thermal comfort for a two-star green building in Wuhan, China. The thermal load of the building before optimization is 36% lower than a typical energy-efficient building of the same size. A total of 19 continuous design variables, including different concrete thicknesses, insulation thicknesses, absorbance of solar radiation for each exterior wall/roof and different window-to-wall ratios for each fa ade, are considered for optimization. The thermal load and annual discomfort degree hours are selected as the objective functions for optimization. Two prediction models, multi-linear regression (MLR) model and an artificial neural network (ANN) model, are developed to predict the building thermal performance and adopted as fitness functions for a multi-objective genetic algorithm (GA) to find the optimal design solutions. As compared to the original design, the optimal design generated by the MLRGA approach helps to reduce the thermal load and discomfort level by 18.2% and 22.4%, while the reductions are 17.0% and 22.2% respectively, using the ANNGA approach. Finally, four objective functions using cooling load, heating load, summer discomfort degree hours, and winter discomfort degree hours for optimization are conducted, but the results are no better than the two-objective-function optimization approach.