A systematic review of modeling method of multi-energy coupling and conversion for urban buildings DOI
Shuo Liu,

Yi Dai,

Xiaohua Liu

и другие.

Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115886 - 115886

Опубликована: Май 1, 2025

Язык: Английский

AI agent-based indoor environmental informatics: Concept, methodology, and case study DOI

Jaemin Hwang,

Sungmin Yoon

Building and Environment, Год журнала: 2025, Номер unknown, С. 112879 - 112879

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

1
CityEL: A web-based platform to support city-scale building energy efficiency based on AutoBPS DOI
Chengcheng Song, Jingjing Yang, Zhiyuan Wang

и другие.

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106147 - 106147

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
AI Agent-Based Intelligent Urban Digital Twin (I-UDT): Concept, Methodology, and Case Studies DOI Creative Commons

Sebin Choi,

Sungmin Yoon

Smart Cities, Год журнала: 2025, Номер 8(1), С. 28 - 28

Опубликована: Фев. 11, 2025

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 consumption via individual use interactions. As virtual model within (UDTs), UBEM offers the potential for managing sustainable cities. However, UDTs face challenges regard integrating large-scale data relying on bottom-up approaches. In this study, we propose an AI agent-based intelligent twin (I-UDT) enhance DTs’ technical realization UBEM’s service functionality. Integrating GPT UDT enabled efficient integration fragmented city-scale extraction features, addressing limitations traditional UBEM. This framework ensures continuous updates streamlined provision updated information users future studies. research establishes I-UDT lays foundation implementations. case studies include (1) analysis, (2) prediction, (3) feature engineering, (4) services 3500 Seoul. Through these studies, was integrated analyzed scattered data, predicted consumption, derived conditioned areas, evaluated benchmark.

Язык: Английский

Процитировано

0
Multi-source data fusion-driven urban building energy modeling DOI

Sebin Choi,

Yi Dong, Deuk-Woo Kim

и другие.

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106283 - 106283

Опубликована: Март 1, 2025

Процитировано

0
Public sentiment analysis of data center energy consumption using social media data and large language models DOI
Hongyu Wang, Weiqi Hua, Jinqing Peng

и другие.

Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115802 - 115802

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
A systematic review of modeling method of multi-energy coupling and conversion for urban buildings DOI
Shuo Liu,

Yi Dai,

Xiaohua Liu

и другие.

Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115886 - 115886

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0