Real- Time Building Energy Modeling and Fault Detection and - PowerPoint PPT Presentation

Real- Time Building Energy Modeling and Fault Detection and Diagnostics for a DoD Building Bing Dong1, Zheng O’Neill2 1 University of T exas, San Antonio, TX, USA 2 University of Alabama, AL, USA The work was done at the United Technologies Research Center ME 4343 HVAC Design

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

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

Building Facts • Each 150K sf2 Barrack – Compartments, classrooms and cafeteria/galley • 7114 Cooling – T wo absorption chiller: 450 ton – Chilled water loop with fjxed-speed 7113 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) 4

T echnology 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 5

T echnology Approaches • Integrated Energy Modeling Approach

T echnology Approaches  BIM to BEM automatic code generation Traditional Approach Building 7114 Architectural Model Building 7114 Mechanical Model BEM (Thermal Network Model) One Week 7

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

T echnology Approaches  Real-time Data Acquisition Outside view sleeping area cafeteria classroom Building Control Virtual Test Bed (BCVTB) Extend BCVTB BACnet actors: Naval Station Great Lakes (Bldg 7114) 1) BACnet reader utility : Automatically generate a.xml confjguration fjle and a .csv point description fjle based on the fjle created by Simens EMS 2) StoreBACnetDatatoBIMDatabase: Based on the .csv fjle, automatically create SQL statements based on the raw data received from EMS 3) DatabaseManager Establish the connection between BCVTB and BIM-based database Simens EMS Our DAQ 9

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

Results  Real-time Energy Simulation BLDG7114 Water Side Load (kW Simulated 1000 Measured 500 0 07/06 07/07 07/08 07/09 07/10 07/11 0.1 Instant Error 0.05 0 -0.05 -0.1 07/07 07/08 07/09 07/10 07/11 Building 7114 Real-Time Simulation Results from 07/06/2011 to 07/11/2011. Building 7114 AHU3 secondary and primary system diagram 11

Results Building 7114 Energy Diagnostics: Economizer fault identifjed and corrected Reference ROM Actual AHU network Expected OA Damper Position 1 OAT Train OAT OAD OAD 0.8 0.6 Airfmow Airfmow 0.4 07/17 07/24 07/31 AHU AHU Anomaly Score energy energy Building Operation data 1000 Damper Damper 500 Inference Valve Valve 0 07/17 07/24 07/31 Building 7114 Energy Impact Operation data 100 MAT 95 OA Damper Temperature (F) / Damper Position (%) OA damper 100% DAT 90 DATS 85 OAT 80 75 70 65 60 DAT setpoint cannot be 55 maintained 07/21 07/26 07/31 Times Faults was corrected on Aug 3rd , 2011. Economizer faults: Measured chilled water energy consumption shows 18% savings were Enthalpy calculation in control sequences is wrong achieved

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 difgerent from what is actually implemented locally. Communication with fjeld people is necessary to get an accurate baseline model. 13

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