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Database Supported Bacnet Data Acquisition System for Building Energy Diagnostics
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This paper reports a tool that can be used to acquire and store the BACnet (A Data Communication Protocol for Building Automation and Control Networks) data for the purpose of building energy system Fault Detection and Diagnostics (FDD). Building Automation Control (BAC) systems have become a common practice in recently constructed buildings in the United States. Although building operational data could readily be collected for various analysis purposes, there is still a debate in building community which or what FDD method is better in terms of performance matrix, such as false alarm rate and training data requirement, etc. Therefore, from the user's perspective, it is potentially beneficial to try out different FDD methods before the deployment, or even develop a dedicated FDD method in a specific case. This is the motivation for development of the BACnet data storage system discussed in this paper, which could then be used together with BACnet data acquisition module in an open source Building Control Virtual Test Bed (BCVTB) environment . This paper discusses (1) Relational database schema development for the purpose of storing building operational data and FDD analysis data (2) Development of the connector in BCVTB that enables the transition from the BACnet module to the database module and (3)Testing of the integrated system in a real building. The relational database is intended to be general and detailed enough so that it can be applied to different buildings and projects with various complexity without any major structure change. The BACnet-reader to database connector enables seamless data flow from commercial BACnet system to user's customized workstation. The integrated system enables users to analyze building operational data in an effective and efficient way, which helps achieve automated FDD in buildings.
Li, Z.; Augenbroe, G. (2011). Database Supported Bacnet Data Acquisition System for Building Energy Diagnostics. Energy Systems Laboratory (http://esl.tamu.edu); Texas A&M University (http://www.tamu.edu). Available electronically from