Please use this identifier to cite or link to this item:
https://doi.org/10.1007/s12273-020-0612-7
Title: | Automated recognition and mapping of building management system (BMS) data points for building energy modeling (BEM) | Authors: | ZHAN SICHENG CHONG ZHUN MIN,ADRIAN BERTRAND LASTERNAS |
Keywords: | Science & Technology Physical Sciences Technology Thermodynamics Construction & Building Technology building management system (BMS) building energy modeling (BEM) auto-mapping DBSCAN metadata interpretation GENE NAME BIM SCHEMA |
Issue Date: | 20-Mar-2020 | Publisher: | TSINGHUA UNIV PRESS | Citation: | ZHAN SICHENG, CHONG ZHUN MIN,ADRIAN, BERTRAND LASTERNAS (2020-03-20). Automated recognition and mapping of building management system (BMS) data points for building energy modeling (BEM). BUILDING SIMULATION 14 (1) : 43-52. ScholarBank@NUS Repository. https://doi.org/10.1007/s12273-020-0612-7 | Abstract: | With the advance of the internet of things and building management system (BMS) in modern buildings, there is an opportunity of using the data to extend the use of building energy modeling (BEM) beyond the design phase. Potential applications include retrofit analysis, measurement and verification, and operations and controls. However, while BMS is collecting a vast amount of operation data, different suppliers and sensor installers typically apply their own customized or even random non-uniform rules to define the metadata, i.e., the point tags. This results in a need to interpret and manually map any BMS data before using it for energy analysis. The mapping process is labor-intensive, error-prone, and requires comprehensive prior knowledge. Additionally, BMS metadata typically has considerable variety and limited context information, limiting the applicability of existing interpreting methods. In this paper, we proposed a text mining framework to facilitate interpreting and mapping BMS points to EnergyPlus variables. The framework is based on unsupervised density-based clustering (DBSCAN) and a novel fuzzy string matching algorithm “X-gram”. Therefore, it is generalizable among different buildings and naming conventions. We compare the proposed framework against commonly used baselines that include morphological analysis and widely used text mining techniques. Using two building cases from Singapore and two from the United States, we demonstrated that the framework outperformed baseline methods by 25.5%, with the measurement extraction F-measure of 87.2% and an average mapping accuracy of 91.4%. | Source Title: | BUILDING SIMULATION | URI: | https://scholarbank.nus.edu.sg/handle/10635/191898 | ISSN: | 1996-3599 1996-8744 |
DOI: | 10.1007/s12273-020-0612-7 |
Appears in Collections: | Staff Publications Elements |
Show full item record
Files in This Item:
File | Description | Size | Format | Access Settings | Version | |
---|---|---|---|---|---|---|
Zhan2020_Article_AutomatedRecognitionAndMapping.pdf | Published version | 1.95 MB | Adobe PDF | CLOSED | Published |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.