Multi-objective optimization framework for the building envelope of public rental housing in China's cold regions

This study aims to improve the energy performance and room environmental performance of public rental housing (PRH) in cold regions of China. PRH plays an important role in providing basic housing security in major cities in China. Proposing low-energy consumption strategies for buildings has always been a research difficulty that needs to be urgently solved. In this study, three-dimensional parametric modeling of modular units of public rental housing is carried out using Grasshopper and Rhino software, and building performance simulation is performed using plug-ins such as Ladybug and Honeybee. The Energy Use Intensity (EUI) is reduced by improving the building envelope, the Thermal Discomfort Percentage (TDP) is reduced, and the Useful Daylight Illuminance (UDI) is increased. Sensitivity analysis is performed using the SRC and TGP methods to reduce minor parameters and redefine key independent variables. Subsequently, a genetic algorithm (GA) with the Octopus plug-in is used for multi-objective optimization based on daylighting, thermal comfort, and energy consumption. The findings indicate that the optimized equilibrium model achieved a reduction of about 20.9 % in EUI, a reduction of about 47.0 % in TDP, and an increase in UDI of about 13.8 % compared with the original model. This integrated approach effectively addresses the challenges of optimizing PRH performance and provides a robust framework for future applications in similar climate conditions.