AI-Driven Green Building Technology Innovation: Knowledge Structure, Evolution Trends, Research Paradigms and Future Prospects DOI Creative Commons
Wu Jie, Qinge Wang, Zhenxu Guo

et al.

Buildings, Journal Year: 2025, Volume and Issue: 15(10), P. 1754 - 1754

Published: May 21, 2025

The rapidly evolving domain of artificial intelligence (AI) is significantly influencing the green building (GB) sector, acting as a catalyst for technology innovation (GBTI). Notably, unlike AI applications in buildings (AI-in-GB), AI-driven GBTI positions central force, promoting and leading novel technological breakthroughs. Although research has been conducted AI-in-GB, there remains lack in-depth analysis on advancements. To address this gap, study comprehensively reviews existing GBTI, systematically organizing analyzing knowledge structure, thematic evolution, paradigms, potential future directions. This conducts bibliometric analyses 151 publications sourced from Scopus using VOSviewer CiteSpace, capturing temporal characteristics, hotspots, frontiers area. Additionally, based dynamic topic modeling, analyzes 86 representative articles, identifying three key themes their evolution trends, elucidating framework within field. Through further discussion, reveals four core paradigms proposes directions, providing theoretical support guidance its continued development. first to focus contributing comprehensive understanding expanding GBTI.

Language: Английский

AI-Driven Green Building Technology Innovation: Knowledge Structure, Evolution Trends, Research Paradigms and Future Prospects DOI Creative Commons
Wu Jie, Qinge Wang, Zhenxu Guo

et al.

Buildings, Journal Year: 2025, Volume and Issue: 15(10), P. 1754 - 1754

Published: May 21, 2025

The rapidly evolving domain of artificial intelligence (AI) is significantly influencing the green building (GB) sector, acting as a catalyst for technology innovation (GBTI). Notably, unlike AI applications in buildings (AI-in-GB), AI-driven GBTI positions central force, promoting and leading novel technological breakthroughs. Although research has been conducted AI-in-GB, there remains lack in-depth analysis on advancements. To address this gap, study comprehensively reviews existing GBTI, systematically organizing analyzing knowledge structure, thematic evolution, paradigms, potential future directions. This conducts bibliometric analyses 151 publications sourced from Scopus using VOSviewer CiteSpace, capturing temporal characteristics, hotspots, frontiers area. Additionally, based dynamic topic modeling, analyzes 86 representative articles, identifying three key themes their evolution trends, elucidating framework within field. Through further discussion, reveals four core paradigms proposes directions, providing theoretical support guidance its continued development. first to focus contributing comprehensive understanding expanding GBTI.

Language: Английский

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