Developing a bottom-up approach to assess energy challenges in urban residential buildings of China
Dawei Xia, Zhuotong Wu, Yukai Zou, Ruijun Chen, Siwei Lou
Abstract
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.