Gupta, Rajesh K.
Kjærgaard, Mikkel Baun
KAUST Grant NumberOSR-2015-Sensors-2707
Permanent link to this recordhttp://hdl.handle.net/10754/667990
MetadataShow full item record
AbstractBuildings account for 32% of worldwide energy usage. A new regime of exciting new “applications” that span a distributed fabric of sensors, actuators and humans has emerged to improve building energy efficiency and operations management. These applications leverage the technological advances in embedded sensing, processing, networking and methods by which they can be coupled with supervisory control and data acquisition systems deployed in modern buildings and with users on mobile wireless platforms. There are, however, several technical challenges to confront before such a vision of smart building applications and cyber-physical systems can be realized. The sensory data produced by these systems need significant curation before it can be used meaningfully. This is largely a manual, cost-prohibitive task and hence such solutions rarely experience widespread adoption due to the lack of a common descriptive schema. Recent attempts have sought to address this through data standards and metadata schemata but fall short in capturing the richness of relationships required by applications. This paper describes Brick, a uniform metadata schema for representing buildings that builds upon recent advances in the area. Our schema defines a concrete ontology for sensors, subsystems and the relationships between them, which enables portable applications. We demonstrate the completeness and effectiveness of Brick by using it to represent the entire vendor-specific sensor metadata of six diverse buildings across different campuses, comprising 17,700 data points, and running eight unmodified energy efficiency applications on these buildings.
CitationBalaji, B., Bhattacharya, A., Fierro, G., Gao, J., Gluck, J., Hong, D., … Whitehouse, K. (2018). Brick : Metadata schema for portable smart building applications. Applied Energy, 226, 1273–1292. doi:10.1016/j.apenergy.2018.02.091
SponsorsFunding: This work was supported by the National Science Foundation [Grant Nos. CPS-1239552, NSF-1636879, IIS-1636916, CSR-1526237, CNS-1526841, NSF-1305362]; the U.S. Department of Energy [Grant No. DE-EE0006353]; the King Abdullah University of Science and Technology award [Grant No. #OSR-2015-Sensors-2707]; the Innovation Fund Denmark [Grant No. 4106-00003B]; the EU H2020 [Grant No. 676760]; and Intel Corporation.