A simulation-based method to predict the life cycle energy performance of residential buildings in different climate zones of China

This paper used a simulation-based method to predict the life cycle energy performance of residential buildings in different climate zones of China. After comparison, GISS-E2-R was selected as the general climate model (GCM) for generating future weather data, and 15 locations in different climate zones of China were involved. To predict the life cycle building performance, we created a Python procedure to generate future weather data files for every year from 2020 to 2099 for each location and performed simulations on a parametric simulation tool. According to the simulation results, heating energy is expected to decrease, and cooling energy is expected to increase in the future under the influence of climate change. The more climate change there is, the greater the heating energy and cooling energy of the building change. There are three trends in residential building energy demand. For the locations where heating energy dominates, there will be a significant decrease in total energy demand. For the locations in central China, the increase in cooling energy will offset part of the decrease in heating energy; therefore, there will be a relatively small drop in the total energy demand. For the locations where cooling energy dominates, the total energy demand is expected to rise. Finally, a simple sensitivity analysis was conducted to explore the impact of the passive design of the window-to-wall ratio (WWR), solar heat gain coefficient (SHGC), wall construction and roof construction on the life cycle energy demand under representative concentration pathway (RCP) 8.5 and 4.5.