Energy-efficient building spatial layout strategies under future climate change scenarios

This study investigates the impact of future climate change on the energy-efficient spatial layout optimization of buildings, addressing a gap in research that primarily focuses on current climate conditions. A novel energy-efficiency-oriented method is proposed for automatically generating and optimizing three-dimensional building layouts. Developed on the Rhino platform with Python, the method utilizes a genetic algorithm to iteratively optimize spatial layouts, minimizing annual average energy consumption. A standard office building model was used as the experimental subject, with future typical meteorological year data (2030–2059) generated for five SSP carbon emission scenarios using a self-developed distribution-adjustment time-mapping downscaling method.

The results indicate that future climate change significantly affects energy optimization through spatial layout strategies. For Shenzhen, the optimal solutions from genetic algorithm optimization showed an average 22.63% increase in annual energy consumption under future scenarios, with only a 6.4% variation across different scenarios. This suggests a general trend of rising energy consumption due to climate change, with relatively small differences among scenarios. Statistical heterogeneity in optimal solutions across scenarios highlights the influence of climate change on energy-saving strategies quantitatively. The findings establish a preliminary correlation between future climate change and energy-efficient spatial layout optimization. Later, we will leverage K-means clustering and statistical methods to analyze solution sets, providing deeper insights into the relationships between climate scenarios and layout strategies.