Multiobjective and hybrid intelligence approaches to enhance human comfort and mitigate greenhouse gas emissions of Chinese courtyard buildings

This research focuses on optimizing the microenvironment parameters of Chinese courtyard buildings in response to urbanization and climate change. Two particle swarm optimization-based approaches were used: multiobjective optimization and hybrid soft computing frameworks. These methods aimed to enhance human comfort and reduce greenhouse gas (GHG) emissions. Using building performance simulation, outdoor thermal parameters were estimated, including the universal thermal climate index, GHG emissions, and discomfort hours. Results showed varied outcomes across cities, with Hangzhou improving outdoor comfort but with poorer indoor comfort, while other cities like Wuhan and Chengdu exhibited different optimal values for GHG emissions and comfort.