The multi-objective optimization of residential building glass in summer-hot and winter-cold regions using genetic algorithms: energy consumption, carbon emissions, and health performance analysis.

Introduction: With the growing demand for environmental sustainability and residential comfort, low-carbon buildings and healthy urban planning have become key research priorities. In summer-hot and winter-cold regions, the performance of residential building glazing plays a critical role in balancing energy efficiency, carbon emissions, and indoor health. Methods: This study investigates the multi-objective optimization of residential building glass using genetic algorithms. Building energy consumption, carbon emissions, and indoor health performance are set as optimization objectives. Key glass parameters-including the window heat transfer coefficient, solar heat gain coefficient, and visible light transmittance-are optimized through a multi-objective genetic algorithm framework. Simulations are conducted using the Rhino and Grasshopper platforms, with Hangzhou, China, selected as the case study area. Results: The optimization results indicate that annual building energy consumption decreases from 50.95 to 40.26 kWh/(m Discussion: By comprehensively considering energy efficiency, carbon emissions, and indoor health performance, this study proposes an optimized glazing configuration for residential buildings in summer-hot and winter-cold regions. The results suggest prioritizing south-facing windows in building design, while adjusting glass parameters for other orientations according to specific conditions. This work provides practical technical support and optimization strategies for the development of low-carbon buildings and healthy cities.