AI Agent-Based Intelligent Urban Digital Twin (I-UDT): Concept, Methodology, and Case Studies

Highlights: What are the main findings? In the developed AI agent-based intelligent digital twin (I-DT), UBEM overcomes the limitations of the traditional UBEM approach and enables efficient analysis of urban building energy. GPT-based UBEM effectively performed the core functions of UBEM, serving as a key technology in I-UDT applications and services. What are the implications of the main findings? The I-UDT enables more accurate and comprehensive urban energy management, supporting the development of sustainable cities and carbon-neutral strategies. Implementing I-UDTs enables urban policymakers to make data-driven decisions, improve energy efficiency, and enhance the scalability of digital twin applications. The concept of digital twins (DTs) has expanded to encompass buildings and cities, with urban building energy modeling (UBEM) playing a crucial role in predicting urban-scale energy consumption via modeling individual energy use and interactions. As a virtual model within urban digital twins (UDTs), UBEM offers the potential for managing energy in sustainable cities. However, UDTs face challenges with regard to integrating large-scale data and relying on bottom-up UBEM approaches. In this study, we propose an AI agent-based intelligent urban digital twin (I-UDT) to enhance DTs’ technical realization and UBEM’s service functionality. Integrating GPT within the UDT enabled the efficient integration of fragmented city-scale data and the extraction of building features, addressing the limitations of the service realization of traditional UBEM. This framework ensures continuous updates of the virtual urban model and the streamlined provision of updated information to users in future studies. This research establishes the concept of an I-UDT and lays a foundation for future implementations. The case studies include (1) data analysis, (2) prediction, (3) feature engineering, and (4) information services for 3500 buildings in Seoul. Through these case studies, the I-UDT was integrated and analyzed scattered data, predicted energy consumption, derived conditioned areas, and evaluated buildings on benchmark.