This research investigates retrofitting strategies for multifunctional spaces within educational buildings,employing agent-based and performance-based modeling to support decision-making.An experimental matrix was dev...This research investigates retrofitting strategies for multifunctional spaces within educational buildings,employing agent-based and performance-based modeling to support decision-making.An experimental matrix was developed,reflecting three usage scenarios(reading,exhibition,lecture)across four retrofitting schemes.An agent-based model was developed to delineate intricate human behaviors in space and examined the self-organizing behaviors of 30 agents for each scheme in every scenario,evaluating six metrics on spatial efficiency and visual experience.Calibrated models,derived from real data and processed through DesignBuilder software,evaluated three metrics:energy use,thermal comfort,and visual comfort.The research then incorporated metrics from the agent-based model and performance simulation to develop a method for discussing the decision-making process in retrofit strategies.The findings indicate that the optimal retrofitting solution for multifunctional spaces is heavily influenced by the distribution of usage scenarios.Given the substantial influence of space metrics on selecting the optimal retrofit scheme,the proposed framework effectively facilitates decision-making for building retrofits by providing a holistic evaluation of both spatial and energy criteria.展开更多
基金sponsored by the National Science and Foundation of China(No.52208011)the Natural Science and Foundation of China(NSFC No.52208010)the China Postdoctoral Science Foundation(No.2022M720716).
文摘This research investigates retrofitting strategies for multifunctional spaces within educational buildings,employing agent-based and performance-based modeling to support decision-making.An experimental matrix was developed,reflecting three usage scenarios(reading,exhibition,lecture)across four retrofitting schemes.An agent-based model was developed to delineate intricate human behaviors in space and examined the self-organizing behaviors of 30 agents for each scheme in every scenario,evaluating six metrics on spatial efficiency and visual experience.Calibrated models,derived from real data and processed through DesignBuilder software,evaluated three metrics:energy use,thermal comfort,and visual comfort.The research then incorporated metrics from the agent-based model and performance simulation to develop a method for discussing the decision-making process in retrofit strategies.The findings indicate that the optimal retrofitting solution for multifunctional spaces is heavily influenced by the distribution of usage scenarios.Given the substantial influence of space metrics on selecting the optimal retrofit scheme,the proposed framework effectively facilitates decision-making for building retrofits by providing a holistic evaluation of both spatial and energy criteria.