Results of multi-objective optimization.

<div><p>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 maximi...

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Main Author:	Juan Ren (733592) (author)
Other Authors:	Yuan Meng (173434) (author), Yu Liu (6938) (author)
Published:	2025
Subjects:	Evolutionary Biology Ecology Biological Sciences not elsewhere classified sustainable residential development rise residential districts rise residential areas regression analysis revealed pronounced seasonal variations outdoor thermal comfort machine learning approach integrated genetic algorithm improving thermal comfort ii genetic algorithm floor area ratio findings provide evidence facing angle deviation convolutional neural networks conducted using nsga comfort contradiction highlights capturing complex non balanced architectural strategies summer overheating risks optimized building morphology yulin </ p building length xlink "> summer utci similar climates results indicate regulatory constraints positive correlation offering implications objective optimization northern shaanxi morphological parameters morphological optimization linear relationships influence mechanisms dual challenge 45 ).
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Results of multi-objective optimization.

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