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Real-Time Building Energy Modeling and Fault Detection and Diagnostics for a DoD Building
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Transcript of Real-Time Building Energy Modeling and Fault Detection and Diagnostics for a DoD Building
ME 4343 HVAC Design
Real-Time Building Energy Modeling and Fault Detection and Diagnostics for
a DoD BuildingBing Dong1, Zheng O’Neill2
1 University of Texas, San Antonio, TX, USA2 University of Alabama, AL, USA
The work was done at the United Technologies Research Center
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• 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)4
7114
7113
Technology Approaches
5
Core Layer: BIM-based DatabaseBIM to BEM Real-time Data Acquisition
Application Layer: Real-time energy simulation, visualization and diagnostics
BIM BasedDatabase
00:00 06:00 12:00 18:00 00:000
200
400
600
800
1000
BLD
G7114 W
ate
r S
ide L
oad (
kW
)
SimulatedMeasured
03:00 06:00 09:00 12:00 15:00 18:00 21:00
-0.1
0
0.1
BLD
G7114 W
ate
r S
ide L
oad (
kW
)
Energy Visualization
Building Reference Model
Energy Diagnostics
PC Running Integrated infrastructurePC Running EMS
BACnetGate Way
Ethernet
HVAC Lighting Weather
Core Applications
BCVTB
Revit
• Overview of the Integrated Infrastructure
Technology Approaches
• Integrated Energy Modeling Approach
Tosur Tisur Tamb Tzone
C
1/hiA 1/hoA R Qsurfi Qsurfo
C
Qstructure
Rwin
BIM Database
y = 6.4017x2 - 331.03x + 3355.6R² = 0.9715
0
5000
10000
15000
20000
25000
30000
35000
40000
0 10 20 30 40 50 60 70 80 90 100
Fan
Pow
er (w
)
Fan Speed (%)
Fan SpeedMeasured PowerPoly. (Fan Speed)
Envelope Model
HVAC Equipment Models
Calibration
Calibration
Total PumpsCooling Tower Fans
AHU Supply
Fans
AHU Return
Fans
Exhaust Fans
Dev -5% -4% -13% -4% -3% -12%
-25%
-20%
-15%
-10%
-5%
0%
Elec
tric
Ene
rgy
Dev
iatio
n
Target ±10% at rated conditions
Model Integration
and Validation
Architectural Model
Mechanical Model
Real-Time Data Acquisition
Technology Approaches
7
BIM to BEM automatic code generation
Traditional Approach
Building 7114 Architectural Model
Building 7114 Mechanical Model
Tosur Tisur Tamb Tzone
C
1/hiA 1/hoA R Qsurfi Qsurfo
C
Qstructure
Rwin
BEM (Thermal Network Model)
One Week
Technology Approaches
8
BIM to BEM automatic code generation
Automatic data extract
IFC BIM Database
Automatic data extract
BEMInput files
Building 7114 Architectural Model
Tosur Tisur Tamb Tzone
C
1/hiA 1/hoA R Qsurfi Qsurfo
C
Qstructure
Rwin
BEM (Thermal Network Model)
Building 7114 Mechanical Model
Our ApproachTraditional Approach
Building 7114 Architectural Model
Building 7114 Mechanical Model
Tosur Tisur Tamb Tzone
C
1/hiA 1/hoA R Qsurfi Qsurfo
C
Qstructure
Rwin
BEM (Thermal Network Model)
One Week < 5 minutes!!
gbXML
Technology Approaches
9
Real-time Data Acquisition
Simens EMSOur DAQ
sleeping area
cafeteria classroom
Outside view
Naval Station Great Lakes (Bldg 7114) Extend BCVTB BACnet actors: 1) 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
3) DatabaseManager Establish the connection between BCVTB and BIM-
based database
Building Control Virtual Test Bed (BCVTB)
10
Results
Real-time Energy Performance Visualization
Building Hierarchy Interface
Time-Series Energy Flows Interface
Energy Statistics Pie Chart Interface
Results
11
Real-time Energy Simulation
1,_1, raz TQ
2,_2 , raz TQ
3,_3 , raz TQ
4,_4 , raz TQ
raz TQ _,
fanQ
fanQ
exam _
rmam _
mixamT _, ambamT _,
HCQ CCQ
samT _,
1
1
5
3
2
4
4
swmT _, rwmT _,
ram _
5
2
2
3
3
56
8
3
6
Zone modeSupply/Return fanMixed airEconomizerHeating coilCooling coil
8 Zone reheat coil
7
7 DAT set-point
9
10
9 Secondary loop pumps10 Bypasss loop
11
11 Primary loop pumps
12
12 Hotwater loop pumps
13
13 Chiller S/R water temp14
14 Condenser pumps
15
15 Cooling tower
07/06 07/07 07/08 07/09 07/10 07/110
500
1000
BLD
G71
14 W
ater
Sid
e Lo
ad (
kW)
SimulatedMeasured
07/07 07/08 07/09 07/10 07/11
-0.1
-0.05
0
0.05
0.1
Inst
ant E
rror
Building 7114 AHU3 secondary and primary system diagram
Building 7114 Real-Time Simulation Results from 07/06/2011 to 07/11/2011.
Results
OAT
AHU energy
OAD
Airflow
Damper
Valve
AHU network1,_1, raz TQ
2,_2 , raz TQ
3,_3 , raz TQ
4,_4 , raz TQ
raz TQ _,
fanQ
fanQ
exam _
rmam _
mixamT _, ambamT _,
HCQCCQ
samT _,
1
1
5
32
4
4
swmT _, rwmT _,
ram _
5
2
2
3
3
56
8
3
6
Zone modeSupply/Return fanMixed airEconomizerHeating coilCooling coil
8 Zone reheat coil
7
7 Discharge air temp set-point
Reference ROM
Building Operation data
Train
Inference
Energy Impact
07/21 07/26 07/31
55
60
65
70
75
80
85
90
95
100
Times
Tem
pera
ture
(F
) /
Dam
per
Pos
ition
(%
)
MAT
OA Damper
DATDATS
OAT
Operation data
OA damper 100%
DAT setpoint cannot be maintained
07/17 07/24 07/31
0.4
0.6
0.8
1OA Damper Position
07/17 07/24 07/310
500
1000
Anomaly Score
07/17 07/24 07/31
0.4
0.6
0.8
1OA Damper Position
07/17 07/24 07/310
500
1000
Anomaly Score
Actual
Expected
Building 7114
Building 7114 Energy Diagnostics: Economizer fault identified and corrected
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
B711
4 CH
W A
vera
ge B
TU/h
rOAT BINS (F)
With Faults
Faults Corrected
Economizer faults: Enthalpy calculation in control sequences is wrong
Faults was corrected on Aug 3rd , 2011. Measured chilled water energy consumption shows 18% savings were 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.
13
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.
• 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?
14
Thank you!