Agentic AI-Enabled Framework for Thermal Comfort and Building Energy Assessment in Tropical Urban Neighborhoods

TL;DR

This paper introduces an agentic AI framework combining large language models with lightweight physics models to evaluate thermal comfort and energy use in tropical urban neighborhoods, enhancing sustainable urban design.

Contribution

It presents a novel AI-enabled reasoning system that integrates LLMs with physics-based models for rapid, accurate microclimate and energy assessments in urban planning.

Findings

Framework reduces computational time for microclimate predictions.

Enables exploration of climate-resilient building strategies.

Demonstrates potential for sustainable urban design applications.

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 fa\c{c}ades 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.