Developing a bottom-up approach to assess energy challenges in urban residential buildings of China

Accurate evaluation on residential energy demand is crucial for sustainable energy systems and urban development. Bottom-up approaches are reliable to capture the building energy characteristics. However, the existing bottom-up approaches require large volumes of high-quality residential building data, which are often inaccessible in developing countries like China. This study proposes a bottom-up approach based on prototype residential units to assess energy challenges in urban residential sector of China. By integrating data collection, variable selection, and K-prototype clustering analysis, the method identifies several residential prototypes that can be used for predictions on energy dynamic variation in residential sector. The proposed method is applied in Guangzhou, a major city in southern China, during heat wave events as a case study. The results indicate that both daytime and nighttime cooling loads in the residential sector are significant and should not be overlooked; peak hourly energy demand typically occurs at 7:00 a.m. and 9:00 p.m. The proposed approach provides a scalable framework for forecasting energy demand, supporting policy and urban planning to reduce consumption while enhancing resilience to extreme weather and understanding of energy challenges in China's urban residential sector.