Sequential learning with active partial labeling for building metadata

Lu Lin, Zheng Luo, Dezhi Hong, Hongning Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Modern buildings are instrumented with thousands of sensing and control points. The ability to automatically extract the physical context of each point, e.g., the type, location, and relationship with other points, is the key to enabling building analytics at scale. However, this process is costly as it usually requires domain expertise with a deep understanding of the building system and its point naming scheme. In this study, we aim to reduce the human effort required for mapping sensors to their context, i.e., metadata mapping. We formulate the problem as a sequential labeling process and use the conditional random field to exploit the regular and dependent structures observed in the metadata. We develop a suite of active learning strategies to adaptively select the most informative subsequences in point names for human labeling, which significantly reduces the inputs from domain experts. We evaluated our approach on three different buildings and observed encouraging performance in metadata mapping from the proposed solution.

Original languageEnglish (US)
Title of host publicationBuildSys 2019 - Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
PublisherAssociation for Computing Machinery, Inc
Pages189-192
Number of pages4
ISBN (Electronic)9781450370059
DOIs
StatePublished - Nov 13 2019
Event6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, BuildSys 2019 - New York, United States
Duration: Nov 13 2019Nov 14 2019

Publication series

NameBuildSys 2019 - Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

Conference

Conference6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, BuildSys 2019
Country/TerritoryUnited States
CityNew York
Period11/13/1911/14/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Renewable Energy, Sustainability and the Environment
  • Building and Construction
  • Architecture
  • Electrical and Electronic Engineering

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