The Building Adapter: Automatic Mapping of Commercial Buildings for Scalable Building Analytics

Technical Report · Tue Sep 28 04:00:00 EDT 2021

DOI:https://doi.org/10.2172/1822357· OSTI ID:1822357

Wang, Hongning ^[1]

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1. Univ. of Virginia, Charlottesville, VA (United States); Computer Science, University of Viriginia

This project creates new solutions for the manual metadata mapping problem: the costly process of creating a match between a building’s sensor data streams and the inputs of a building analytics engine. This goal is achieved by creating and improving techniques for metadata inference: automatically constructing new contextual information for sensing and control points based on the sensor point names and the raw time series values. The objective is to enable vendors to apply building analytics to 90% of buildings with no manual mapping, and to 10% of buildings with a 90% reduction in manual mapping. These targets are set for all types of metadata required by current analytics engines, including type, location, equipment type, and other relationships. The outcome of this project is a suite of solutions to the manual mapping problem collectively called the Building Adapter that allows vendors to apply analytics engines to new buildings at a significantly reduced cost.

• Research Organization: Univ. of Virginia, Charlottesville, VA (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
• DOE Contract Number: EE0008227
• OSTI ID: 1822357
• Report Number(s): DE-EE0008227
• Country of Publication: United States
• Language: English

Related Subjects

96 KNOWLEDGE MANAGEMENT AND PRESERVATION
building metadata mapping
building metadata normalization
point name parsing
relation inference
type classification