Sequential Learning with Active Partial Labeling for Building Metadata

Conference · Wed Nov 13 04:00:00 EST 2019

DOI:https://doi.org/10.1145/3360322.3360866· OSTI ID:1567729

Lin, Lu ^[1]; Luo, Zheng ^[2]; Hong, Dezhi ^[2]; Wang, Hongning ^[2]

---

1. University of Virginia; University of Virginia
2. University of Virginia

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.

• Research Organization: University of Virginia
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
• DOE Contract Number: EE0008227
• OSTI ID: 1567729
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
Sequence Labeling
Metadata Inference
Smart Buildings