An integrated genetic algorithm-machine learning approach for morphological optimization of high-rise residential districts in Yulin.

ABSTRACT: The pursuit of global carbon neutrality necessitates addressing the dual challenge of enhancing solar energy utilization while improving thermal comfort in high-rise residential areas, particularly in Yulin, northern Shaanxi, China, where abundant solar resources exist but maximizing solar acquisition often compromises summer thermal environment quality. This resource-comfort contradiction highlights the need for balanced architectural strategies in regions with pronounced seasonal variations. Building morphological parameter optimization is crucial for balancing annual solar energy capture against summer overheating risks, yet research remains insufficient. This study developed parametric layout models using Rhino-Grasshopper, considering key parameters including building length, width, h

SUBMITTER: Ren J 

PROVIDER: S-EPMC12404440 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature