🤖 AI Summary
This study addresses the limitations of existing assessment tools—particularly their lack of interactivity and computational efficiency—in tackling urban heat island effects and high building energy consumption in tropical cities. The authors propose an intelligent agent framework that integrates a large language model (LLM) with lightweight physical models. Through prompt engineering, the LLM is guided to comprehend design tasks, retrieve relevant policy knowledge, and orchestrate microclimate and energy simulation models. This enables rapid, interpretable, and computationally frugal joint evaluation of thermal comfort (based on Physiological Equivalent Temperature, PET) and building energy use. By uniquely combining the LLM’s autonomous reasoning capabilities with physics-based simulations, the approach efficiently validates mitigation strategies such as green walls and cool coatings, demonstrating significant improvements in both thermal comfort and energy efficiency.
📝 Abstract
In response to the urban heat island effects and building energy demands in Singapore, this study proposes an agentic AI-enabled reasoning framework that integrates large language models (LLMs) with lightweight physics-based models. Through prompt customization, the LLMs interpret urban design tasks, extract relevant policies, and activate appropriate physics-based models for evaluation, forming a closed-loop reasoning-action process. These lightweight physics-based models leverage core thermal and airflow principles, streamlining conventional models to reduce computational time while predicting microclimate variables, such as building surface temperature, ground radiant heat, and airflow conditions, thereby enabling the estimation of thermal comfort indices, e.g., physiological equivalent temperature (PET), and building energy usage. This framework allows users to explore a variety of climate-resilient building surface strategies, e.g., green façades and cool paint applications, that improve thermal comfort while reducing wall heat gain and energy demand. By combining the autonomous reasoning capacity of LLMs with the rapid quantitative evaluation of lightweight physics-based models, the proposed system demonstrates potential for cross-disciplinary applications in sustainable urban design, indoor-outdoor environmental integration, and climate adaptation planning. The source code and data used in this study are available at: https://github.com/PgUpDn/urban-cooling-agent.