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Quantitative framework for causality evaluation in data-driven model:A case study of a cooling tower system

Jin Hong Kim Chul Hong Park Seon Young Heo Cheol Soo Park

建筑模拟（英文版）2026，Vol.19Issue(4)：903-919,17.

建筑模拟（英文版）2026，Vol.19Issue(4)：903-919,17.DOI:10.1007/s12273-026-1440-1

Quantitative framework for causality evaluation in data-driven model:A case study of a cooling tower system

Jin Hong Kim ¹Chul Hong Park ¹Seon Young Heo ¹Cheol Soo Park²

作者信息

1. Department of Architecture and Architectural Engineering,College of Engineering,Seoul National University,1,Gwanak-ro,Gwanak-gu,Seoul,08826,Republic of Korea
2. Department of Architecture and Architectural Engineering,Institute of Construction and Environmental Engineering,Institute of Engineering Research,College of Engineering,Seoul National University,1,Gwanak-ro,Gwanak-gu,Seoul,08826,Republic of Korea
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摘要

关键词

causality quantification/cooling tower/representational fidelity/dynamic fidelity/physics-based model/artificial neural network/transfer learning

Key words

causality quantification/cooling tower/representational fidelity/dynamic fidelity/physics-based model/artificial neural network/transfer learning

引用本文复制引用

Jin Hong Kim,Chul Hong Park,Seon Young Heo,Cheol Soo Park..Quantitative framework for causality evaluation in data-driven model:A case study of a cooling tower system[J].建筑模拟（英文版）,2026,19(4):903-919,17.

基金项目

This research was sponsored by the Samsung C&T research grant program.Open Access funding enabled and organized by Seoul National University. （）

建筑模拟（英文版）

ISSN：1996-3599

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