Deploying data driven applications in smart buildings: overcoming the initial onboarding barrier using machine learning

David Waterworth*, Subbu Sethuvenkatraman, Quan Z. Sheng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


The introduction of smart building technology promises many operational and productivity benefits and enables smart grid integration. A significant barrier to deploying smart building applications is mapping the building sensor metadata to the requirements of the smart building applications. In this paper, we have studied the problem of weakly supervised machine learning to accelerate the metadata mapping process, and demonstrated that weak supervision is a promising approach, which to the best of our knowledge, has not been studied in this context. We developed a pattern-based workflow that enables subject matter experts to craft simple rules that process the idiosyncratic text found in building sensor metadata. Our method was validated using three medium-sized commercial office buildings and a public dataset. We worked closely with a team of commercial building subject matter experts to develop and validate our workflow. We compared our approach to active learning and discovered that weak supervision appears to scale better with building size. Subject matter experts overwhelmingly preferred this approach compared to manual methods, and our study with three buildings shows that weak supervision can reduce the time to annotate the metadata in building systems by a factor of 4.

Original languageEnglish
Article number112699
Pages (from-to)1-10
Number of pages10
JournalEnergy and Buildings
Publication statusPublished - 15 Jan 2023


  • Smart buildings
  • Metadata mapping
  • Building analytics deployment
  • Machine learning
  • Weak supervision
  • Label functions
  • Templates


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