This study addresses the multi-objective optimization of building thermal performance in hot summer and cold winter regions, focusing on the conflicts between energy-saving measures during extreme seasons and the often-overlooked transition periods. Taking a community hospital in Wuhan as a case study, 8000 sets of design parameters were generated using Latin Hypercube Sampling, and the relationships between energy consumption and thermal comfort across the entire year and different seasons were explored through correlation analysis. The non-dominated sorting genetic algorithm-III was employed to obtain 184 Pareto front solutions, from which 10 optimal parameter sets were selected. The results demonstrate that optimizing the natural ventilation setpoint to 24 °C significantly reduces heating energy consumption with minimal impact on cooling, while also improving thermal comfort during transition seasons. The 10 optimal solutions achieved a 7.6% reduction in energy consumption and a 21.3% decrease in thermal discomfort hours. This research provides theoretical and practical insights for optimizing building thermal performance through early design adjustments under similar climatic conditions, demonstrating significant application value.
Zhou et al. (Sun,) studied this question.