Achieving carbon neutrality is a critical global goal, with urban building energy modeling (UBEM) playing a pivotal role by providing data-driven insights to optimize energy consumption and reduce emissions. This paper introduces GPT-based urban building energy modeling (GPT-UBEM), a novel approach utilizing GPT’s advanced capabilities to address key UBEM challenges using GPT-4o. The study aimed to demonstrate the effectiveness of GPT-UBEM in performing UBEM tasks and to explore its potential in overcoming traditional limitations. Specifically, (1) basic analytics of urban data, (2) data analysis and energy prediction, (3) building feature engineering and optimization, and (4) energy signature analysis were conducted in four case studies. These analyses were applied to 2,000 buildings in Seoul and 31 buildings in Gangwon-do, South Korea. Through case study, the findings highlighted the ability of GPT-UBEM to integrate diverse data sources, optimize building features for high accuracy in prediction models, and provide valuable insights for urban planners and policymakers through the use of expert domain knowledge and intervention. Additionally, based on the results derived from GPT-UBEM in this study, the current limitations of GPT-UBEM (L1 to L3) and future research directions (F1 to F4) have been outlined.

中文翻译：

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基于 GPT 的数据驱动城市建筑能源建模 （GPT-UBEM）：概念、方法和案例研究


实现碳中和是一项重要的全球目标，城市建筑能源建模 （UBEM） 通过提供数据驱动的见解来优化能源消耗和减少排放，发挥着关键作用。本文介绍了基于 GPT 的城市建筑能源建模 （GPT-UBEM），这是一种利用 GPT 的高级功能来解决使用 GPT-4o 解决关键 UBEM 挑战的新方法。该研究旨在证明 GPT-UBEM 在执行 UBEM 任务方面的有效性，并探索其在克服传统限制方面的潜力。具体来说，（1） 城市数据的基本分析，（2） 数据分析和能源预测，（3） 建筑特征工程和优化，以及 （4） 能源特征分析在四个案例研究中进行。这些分析应用于首尔的 2,000 座建筑物和韩国江原道的 31 座建筑物。通过案例研究，研究结果强调了 GPT-UBEM 整合各种数据源、优化建筑特征以实现预测模型的高精度以及通过使用专家领域知识和干预为城市规划者和政策制定者提供有价值的见解的能力。此外，根据本研究中 GPT-UBEM 得出的结果，概述了 GPT-UBEM 的当前局限性（L1 到 L3）和未来的研究方向（F1 到 F4）。