Neural topic modeling of machine learning applications in building: Key topics, algorithms, and evolution patterns

Abstract

The application of machine learning (ML) in the building domain has rapidly evolved due to developments in ML algorithms. Abundant studies have reviewed the use of ML algorithms to address building-domain-related challenges, but some research questions remain unclear: (i) what is the landscape of ML application topics in building domain, (ii) what are the preferences among different ML application topics and algorithms, and (iii) how these topics, ML algorithms, and their preferences evolve until forming current landscape. To address these aspects, an ML-based topic modeling (TM) approach was used in this paper to identify all ML application topics, elucidate the horizontal correlation and vertical knowledge hierarchy among the topics to reveal their static correlation and dynamic evolution with ML algorithms. Several findings that answered each research question were drawn, and recommendations that can facilitate balanced and rational ML advancements in the building domain are proposed for future research.