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Real-Time Building Energy Modeling and Fault Detection and Diagnostics for a DoD Building

Real-Time Building Energy Modeling and Fault Detection and Diagnostics for a DoD Building. Bing Dong 1 , Zheng O’Neill 2 1 University of Texas, San Antonio, TX, USA 2 University of Alabama, AL, USA. The work was done at the United Technologies Research Center. Introduction. Motivation.

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Real-Time Building Energy Modeling and Fault Detection and Diagnostics for a DoD Building

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  1. Real-Time Building Energy Modeling and Fault Detection and Diagnostics for a DoD Building Bing Dong1, Zheng O’Neill2 1 University of Texas, San Antonio, TX, USA 2 University of Alabama, AL, USA The work was done at the United Technologies Research Center

  2. Introduction • Motivation Source: NBI report 2008 Energy Performance of LEED For New Construction Buildings

  3. Introduction • HVAC systems consume >20% more energy than design intent • Equipment performance degradation, and interact with other systems. • Existing control and information systems do not make visible system level energy consumption. • Need for a scalable building energy management system that includes whole building energy diagnostics and visualization • Better HVAC operational controls and energy diagnostics • Raises the visibility of energy performance to help decision making

  4. Building Facts • Each 150K sf2 Barrack • Compartments, classrooms and cafeteria/galley • Cooling • Two absorption chiller: 450 ton • Chilled water loop with fixed-speed primary pump • Heating • Steam from the base wide central heating plant • steam to water heat exchanger • 5 AHUs for each building • More than 200 VAV boxes with reheat coil • A distributed Direct Digital Control System (DDC) 7114 7113

  5. Technology Approaches • Overview of the Integrated Infrastructure • Core Layer: • BIM-based Database • BIM to BEM • Real-time Data Acquisition • Application Layer: • Real-time energy simulation, visualization and diagnostics

  6. Technology Approaches • Integrated Energy Modeling Approach

  7. Technology Approaches • BIM to BEM automatic code generation Traditional Approach Building 7114 Architectural Model Building 7114 Mechanical Model BEM (Thermal Network Model) One Week

  8. Technology Approaches • BIM to BEM automatic code generation Traditional Approach Our Approach Automatic data extract Building 7114 Architectural Model Building 7114 Architectural Model BIM Database IFC gbXML Automatic data extract Building 7114 Mechanical Model BEM Input files Building 7114 Mechanical Model BEM (Thermal Network Model) BEM (Thermal Network Model) < 5 minutes!! One Week

  9. Technology Approaches • Real-time Data Acquisition Outside view sleeping area Building Control Virtual Test Bed (BCVTB) Extend BCVTBBACnet actors: BACnet reader utility: Automatically generate a.xml configuration file and a .csv point description file based on the file created by Simens EMS 2) StoreBACnetDatatoBIMDatabase: Based on the .csv file, automatically create SQL statements based on the raw data received from EMS DatabaseManager Establish the connection between BCVTB and BIM-based database Naval Station Great Lakes (Bldg 7114) cafeteria Simens EMS Our DAQ classroom

  10. Results • Real-time Energy Performance Visualization Energy Statistics Pie Chart Interface Building Hierarchy Interface Time-Series Energy Flows Interface

  11. Results • Real-time Energy Simulation Building 7114 Real-Time Simulation Results from 07/06/2011 to 07/11/2011. Building 7114 AHU3 secondary and primary system diagram

  12. Results Building 7114 Energy Diagnostics: Economizer fault identified and corrected Reference ROM Train AHU network OAT OAD Airflow Building Operation data AHU energy Damper Inference Valve Building 7114 Energy Impact Operation data OA damper 100% DAT setpoint cannot be maintained Faults was corrected on Aug 3rd , 2011. Measured chilled water energy consumption shows 18% savings were achieved Economizer faults: Enthalpy calculation in control sequences is wrong

  13. Conclusion • This study has demonstrated an integrated infrastructure which integrates design information, database and real-time data acquisition in a real building to support energy modeling, visualization and FDD. • Observations and Lessons learned: • Manually mapping BMS points of each HVAC component. • The designed control logic in the HVAC control system is usually different from what is actually implemented locally. Communication with field people is necessary to get an accurate baseline model.

  14. Acknowledgements: • DoD ESTCP program manager: Dr. Jim Galvin • UTRC: Dong Luo, Madhusudana, Shashanka ,Sunil Ahuja, Trevor Bailey • Naval Station Great Lakes • Energy manager: Peter Behrens • Mechanical Engineer: Kirk Brandys • Facility team • Questions? Thank you!

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